#contextual intent data

[canmom]

using LLMs to control a game character's dialogue seems an obvious use for the technology. and indeed people have tried, for example nVidia made a demo where the player interacts with AI-voiced NPCs:

this looks bad, right? like idk about you but I am not raring to play a game with LLM bots instead of human-scripted characters. they don't seem to have anything interesting to say that a normal NPC wouldn't, and the acting is super wooden.

so, the attempts to do this so far that I've seen have some pretty obvious faults:

relying on external API calls to process the data (expensive!)

presumably relying on generic 'you are xyz' prompt engineering to try to get a model to respond 'in character', resulting in bland, flavourless output

limited connection between game state and model state (you would need to translate the relevant game state into a text prompt)

responding to freeform input, models may not be very good at staying 'in character', with the default 'chatbot' persona emerging unexpectedly. or they might just make uncreative choices in general.

AI voice generation, while it's moved very fast in the last couple years, is still very poor at 'acting', producing very flat, emotionless performances, or uncanny mismatches of tone, inflection, etc.

although the model may generate contextually appropriate dialogue, it is difficult to link that back to the behaviour of characters in game

so how could we do better?

the first one could be solved by running LLMs locally on the user's hardware. that has some obvious drawbacks: running on the user's GPU means the LLM is competing with the game's graphics, meaning both must be more limited. ideally you would spread the LLM processing over multiple frames, but you still are limited by available VRAM, which is contested by the game's texture data and so on, and LLMs are very thirsty for VRAM. still, imo this is way more promising than having to talk to the internet and pay for compute time to get your NPC's dialogue lmao

second one might be improved by using a tool like control vectors to more granularly and consistently shape the tone of the output. I heard about this technique today (thanks @cherrvak)

third one is an interesting challenge - but perhaps a control-vector approach could also be relevant here? if you could figure out how a description of some relevant piece of game state affects the processing of the model, you could then apply that as a control vector when generating output. so the bridge between the game state and the LLM would be a set of weights for control vectors that are applied during generation.

this one is probably something where finetuning the model, and using control vectors to maintain a consistent 'pressure' to act a certain way even as the context window gets longer, could help a lot.

probably the vocal performance problem will improve in the next generation of voice generators, I'm certainly not solving it. a purely text-based game would avoid the problem entirely of course.

this one is tricky. perhaps the model could be taught to generate a description of a plan or intention, but linking that back to commands to perform by traditional agentic game 'AI' is not trivial. ideally, if there are various high-level commands that a game character might want to perform (like 'navigate to a specific location' or 'target an enemy') that are usually selected using some other kind of algorithm like weighted utilities, you could train the model to generate tokens that correspond to those actions and then feed them back in to the 'bot' side? I'm sure people have tried this kind of thing in robotics. you could just have the LLM stuff go 'one way', and rely on traditional game AI for everything besides dialogue, but it would be interesting to complete that feedback loop.

I doubt I'll be using this anytime soon (models are just too demanding to run on anything but a high-end PC, which is too niche, and I'll need to spend time playing with these models to determine if these ideas are even feasible), but maybe something to come back to in the future. first step is to figure out how to drive the control-vector thing locally.

[delesaria-blog]

🌸List five things that make you happy, then put this in the askbox of other people (if you want to). Let's spread some positivity and joy today!🌸

I was also tagged by these lovelies:

@thisonelikesaliens @telomeke @hughungrybear @watchthisqqq

This is not in order, but here goes-

BL: I'm hopelessly in love with Secrets Happened on Litchi Island, and eternally grateful to the people posting about it. Especially @thisonelikesaliens who makes delightful notes that contextualize the show and also the incredible gif creators like @my-rose-tinted-glasses and @save-the-data.

2. Food: My grandmother just made a ton of cannelloni for my brother's birthday, so I'm reaping the benefits. Not an actual picture since we devoured most of it already, but here's what it looks like:

3. Our cats: We found two feral, very sick kittens and had no intention of keeping them. But by the time we got them healthy enough to give them up for adoption, it was too late. I was already in love:

4. Travel: I visited a friend and she took me to Philadelphia's Magic Gardens, which I adored. (photo from official website)

5. My students: I've been teaching for more than a decade, but I'm always learning from them. Recently I had one student make me laugh so hard I fell out of my chair in class.

[fionacle]

I gotta shorten it I think but here's my response for linguistics class on the Tamarian language from the Star Trek: The Next Generation episode "Darmok", this was really fun to make and i’m kinda proud of my analysis:

In the Star Trek, The Next Generation episode “Darmok”, the Tamarian species is introduced along with its very strange language. Their language is different to human language in that it is nonspecific, and has no words to refer to oneself. This is because of its other difference, which is that it uses metaphors instead of just saying what one means. Every phrase they have is a person or place in a certain location or time. For instance, “The river Temarc in winter” means something like “be quiet”, as the captain uses the phrase to tell someone to stop talking, alluding to the silence of a frozen river. They also use shortened versions of these, as he also says “The river Temarc” to mean the same thing, and while trying to offer peace to Picard, he uses “Darmok and Jalad at Tanagra” and “Darmok and Jalad” interchangeably as well. The first point of understood communication between a Tamarian and another species is when their captain gives Picard a burning stick from his campfire so he won’t be cold, saying “Temba, his arms wide”, where Picard deciphers that this refers to a person having his arms open in generosity, being a metaphor for giving him a gift. Picard then picks up the stick and says thank you, opening his arms to relate his words to the metaphor, and the captain smiles in relief and understanding. The captain also said “Shaka when the walls fell” when Picard failed to make a fire himself at first, and later repeats the phrase. Picard pieces that together with their failure to defeat an enemy, and asks “Is that a failure? An inability to do something?” The captain then says “Uzani, his army with fists open! His army with fists closed!” And Picard deciphers that the first part means to lure in the enemy by spreading out, and the second means to attack. From his experience trying to understand Tamarian phrases, he figures out that “That’s how [they] communicate, isn’t it, by citing example! By metaphor!” The captain then exclaims “Sokath, his eyes uncovered!”, which contextually, and since uncovering one’s eyes means they can see, must mean understanding. Later Picard asks the captain to explain the meaning behind the phrases, bringing up the ones he knows, figuring out that certain words are names or locations, and the captain repeats relevant phrases, from which Picard pieces together the story of “Darmok and Jalad at Tanagra”, two Tamarians who came to an island and were brought together by fighting a common foe. This story contextualizes the language in that each phrase is like a sentence in a story book, connected to that part in the story. This particular story explains “Darmok on the ocean” as loneliness, as he started out his journey alone; “Darmok and Jalad at Tanagra” as cooperation, as they work together while on the island; and “Darmok and Jalad on the ocean” as people coming together through defeating a challenge, as they leave together. (same paragraph, character limit->)

Their high regard for these myths and stories is why Picard was beamed down with the captain in the first place, because the captain hoped they would come together in cooperation to defeat the creature on the planet, paralleling Darmok’s story. Troi, an empath, sensed at the beginning that the Tamarians had only good intentions, and she and Data listen back to a recording of their call with the Tamarians, searching databases for the words they hear: “Darmok” and “Tanagra” from “Darmok at Tanagra”. Later explaining their findings with the rest of the crew, Data says they use “narrative imagery” to communicate, and Troi compares their speech to if she said “Juliet on her balcony”, which Crusher says would mean “An image of romance.” This is where it does relate to human language, as Data says “The situation is analogous to understanding the grammar of a language but not the vocabulary”, and Crusher adds “If I didn’t know who Juliet was, or what she was doing on that balcony, the image alone wouldn’t have any meaning.” Troi brings up the particular phrase they’re trying to decipher, saying “For instance, we know that Darmok was a great hero, a hunter, and that Tanagra was an island, but that’s it. Without the details, there’s no understanding.” and Data says “It is necessary for us to learn the narrative from which the Tamarians draw their imagery.” Tamarian language is similar to human language, in that it has a definitive structure. Picard relates Darmok’s story to the epic of Gilgamesh, where people came together to defeat an enemy. Trying to bridge their language gap by comparison, and to help the captain understand his words, he says “Gilgamesh and Enkidu at Uruk”. He also later says “More familiarity with our own mythology might help us to relate to theirs.” He then comes back and calls the rest of the Tamarian crew to tell them he’s learned some of their language, desires peace, and that their captain died, and one of them says “Picard and Dathon at El-Adrel”, showing they do have names for present-day people, or at least nouns that can refer to them, and that when new events come up they refer to them by phrasing them like their current phrases. This shows their sentence structure to be names, optionally with other names, at places, or doing activities. This single sentence shows that they don’t just say events that happened because that’s what they were taught, but that there is also a structure to how they say it, and that they are capable of making new phrases and words as long as it fits this structure. This is like English, in that we have sets of words we already use, like Darmok can be replaced with Picard we can make new nouns and verbs. Their adherence to saying “at” or “with” is comparable to our closed class words, like determiners. English also uses metaphors, even, as at the end Picard is reading the Homeric Hymns and says it’s “…one of the root metaphors of our own culture.” All our words only have meaning because we’re told it, and like Picard deciphering “Temba, his arms wide”, we can figure out meaning through context clues, so we don’t have to know the etymology of every word.

The Tamarian’s language is a possible language for a species to have. I went back and forth on this a lot, because there are a lot of reasons to the contrary. The universal translator transfers another’s thoughts into one’s own language, so it should be able to do the same for this language and simplify out the metaphors into proper English. Its inability to do this is a sign their language is incomplete. Also, since it’s so nonspecific, certain important things would be unable to be communicated, like “Chris needs you to grab a wrench out of the blue toolbox”. Finally, you need a language to tell these stories and say the metaphors with, something with other words to give the full picture. Originally the first and last points were going to make my final answer be that their odd way of speaking may not be a language at all, but rather just an odd way of speaking while using another language. In the end, I changed my mind, because like I said before, human language has its own metaphors and etymologies. People don’t all know “mad as a hatter” refers to hat makers in the past getting poisoned from the mercury they used in their hats, yet we all know it means someone is very, very crazy. We also have words borrowed from other languages, like ballet is from French, and you could go further than that to find out the ways words were changed throughout the years to morph into that form. This means that there could at one point have been another Tamarian language, or even that they borrowed from another species’ language, and that its morphing into its current form is valid as its own separate language. Also, communicating complex ideas shouldn’t be hard for someone well-versed in the language, and we may not have even seen the most complex phrases as they were talking to foreigners, and we all try to simplify our words and sentences when speaking to those who don’t know our language at all. We actually communicate complex things all the time, with memes. There is a screenshot from Sonic where Eggman walks into a room with Rouge the Bat and Shadow the Hedgehog in it, and in a popular fan dub of the game the line said at that moment is “What are you two f*cking talking about?” This means someone can send a textless screenshot of this moment, and communicate that they either don’t understand what’s being said, or that even though they do understand they think the topic is absurd. Children could acquire such a language by being introduced to it by their elders, but would not come up with it themselves as they’d need the historical context explained to hold any value in the metaphors. Picard learned Darmok’s story through noting the context in and the tone with which the captain repeated the phrases, and then the captain put them in order and Picard drew out the tale with a rock and a stick in the sand.

[nuri148]

Bad Boy: No one calls Levi "IT".

I wanted to comment on something that I've seen these days in some meta posts of Bad Boy, but I haven't had the time till now to check the font to confirm my impressions and, you know, argue with receipts.

No one calls Levi "IT".

I also must say, the posts authors are without fault. The "it" in the (rushed, fan-made) English translation is either a mistake (I suspect machine translation might be involved) or a very unfortunate choice of words if translated manually.

You see, there is no "it" in Japanese. There is “this” and “that”, but no “it”.

The word the criminals use is “こいつ” [koitsu] which is a derisive term to refer to a person. Depending on context, it could be translated as “this guy”, “this dude”, “the SOB”, “ that MF”… you get the idea.

If you watch subbed anime, you’ll hear koitsu* all the time in shounen where guys are or want to appear tough – especially since, much like in English, such terms can be used insultingly, informally, or even as a term of endearment between very close people (as in “This mf and I have been bffs since kindergarten!”). Those guys likely use koitsu on a daily basis. I don’t have all the raws handy and neither have the time to go through them, but for instance, Hannes calls Eren koitsu in chapter 1 when he ponders about the kid’s wish to join the SC; and Connie calls Jean this when he “blames” him for his joining the SC in chapter 3.

There are worse ways to call people, and Levi himself uses a wide range of them throughout the main manga.

More importantly, although koitsu can be used for inanimate objects, this use is rather an endearing form in familiar settings. What’s important to note is that koitsu is not the Japanese “it” equivalent by any means and, therefore, it does not carry the dehumanizing undertone that calling a person “it” in English has.

Depending on context (and Japanese is a highly contextual language) it can range from very informal to plain derogatory, but it is absolutely not akin to call a person an object. Calling someone koitsu in the wrong context wouldn’t make them go “omg they’re dehumanizing me”, but rather “omg they called me something very rude.”

Of course this doesn’t mean the criminals in Bad Boy are any less perverse, but I think a clarification of this (I want to believe, honest) mistranslation is important for two reasons: one, to provide an accurate analysis so as not to atribute the characters, or the author, an intent that isn’t there; and two, to not give the haters an excuses to dismiss or disparage a whole meta post because of one incorrect piece of data.

*I’m using only koitsu throughout the text for simplicity’s sake but you’ll also see/hear the equivalent terms soitsu/aitsu/yatsu, which are basically the same, the only difference being the itsu in relation to the speaker: this mf, that mf, that mf over there... You also have doitsu as the interrogative form (which mf?).

Here is a very clear article regarding the use of these words:

[absolutebirth]

READ THIS POST ON NEOCITIES AT ABSOLUTEBIRTH.NEOCITIES.ORG

Absolute Destinty: Analysis is a biweekly blog analysing Revolutionary Girl Utena as a I conduct my third watch-through in summer 2024. My three priorities are to A) create a guide suitable for following along with a first watchthrough which is spoiler-free enough to not have the show spolied but which might highlight hints toward the cumulative storyline, B) knit together a full analysis of Utena which tackles the show's main themes, with an emphasis on my personal interests of surveillance, spectacle, and power, or the questions who sees? what do they see? and who decides? and C) maintain a section at the end of each blog post with the intent of pointing out, for people who are on their second or third watchthroughs, my favorite allusions to the End of the World.

Each post will be, following my favorite quote from the episode, split into 7 sections:

Episode Summary: what it says on the tin

A Wider Gaze: An attempt to put this episode into the wider view of the show as a whole, contextualizing the development of characters, themes, and relationships in Ohtori academy, and then taking that wide view of the show and applying it to the world as we live it-- the world outside of Ohtori academy.

Institutionalized: Drawing from The Shawshank Redemption (1994), here we look at how the characters in each episode have found themselves embedded in Ohtori, enacting actions against their own better interests for fear of leaving behind a comfortable confinement.

The Eye That Fucks the World: Donna Harraway describes ubiquitous surveillance as "the god-trick of seeing everywhere from nowhere" and goes on to say that "this eye fucks the world." Revolutionary Girl Utena is a story about being watched, not only by the viewer on the other side of the camera but, as we will eventually find, by another eye-- which certainly has intentions to fuck the world. Here we see how this ethos of watching and being watched plays out, episode to episode.

Who Decides Who Decides?: Ohtori is a world of power. As the student council squabbles, power shifts between them are some of the most significant developments in each episode. Here, we check in with the power balance, what changes it, and what that means for both Utena and Utena.

Saito Solace: A piece of Saito's manga art from The Gallery At Empty Movement, to finish off the spoiler-free section of the post.

(SPOILERS) The End of the World: The section for those in the know and my kicking-my-feet giggled realizations about the clues Ikuhara leaves us throughout the first 35 episodes. I don't want to say too much here, because I don't want someone who isn't looking for it to read too much, but suffice to say that Utena is certainly one of those stories where hindsight is 20-20.

In terms of my own background, Utena related and otherwise: I was born and raised in the northern Midwestern United States and moved out east for college, where I'm majoring in "data economy" (a self-made amalgamation of economics and computer science) and ethics. I was into anime in high school, mostly grew away from my obsession, but recently viscerally remembered the awe inspiring masterpiece that is Utena and decided it was probably once again time to think about Anthy at least twelve hours a day. My favorite other pieces of media are Disco Elysium, Against Me!'s album Transgender Dysphoria Blues, Don Delilio's novel White Noise, Infinite Jest, Neutral Milk Hotel's In an Aeroplane Over the Sea... in terms of other seminal pieces of lesbian liturature, Fried Green Tomatoes at the Whistle Stop Cafe and everything by Alison Bechdel, but especially Dykes to Watch Out For.

My favorite Utena characters are and always have been Mikage, Anthy, Nanami, and Saionji (in that order) but I hold a soft spot for every single one of them and the spider-thin lines connecting them emotionally and thematically. I believe thinking about the duelists in terms of right and wrong or our id-pol concepts of oppression hinders a reading of the show, just as a flat understanding of those things hinders meaningful connections and community building across the false lines the patriarchy creates, so I generally find myself more sympathetic to Touga, Mikage, and Saionji than others may be. Finally: I suspect, although I haven't cracked their code, that the Nanami episodes are the most significant in the entire show.

The first post, for the first episode, will be posted this Wednesday, May 22nd, 2024.

[miirshroom]

Shadow of the Erdtree Foreshadowing that I haven't seen mentioned much

Some of the clues that the writers knew the direction that the DLC was headed in during development before Elden Ring was released in 2022:

Jarburg was one of the last elements finished in the base game, with Jar Bairn being added in patch 1.03 on March 17th. One of the NPC's who significantly ties into Radahn's questline is Warrior Jar Alexander - mentioned by Jar Bairn - who presumably scoops Radahn's flesh into a pot after the festival concludes. Guess what also changed in patch 1.03? Radahn's hitboxes were stealth adjusted, making him easier to kill. The DLC would then go on to heavily feature pots and Potentates. And Radahn.

Radahn is actually just as mysterious as Godwyn, in that neither of them are ever able to directly speak for themselves as to what are their ambitions. But Radahn has an edge in importance: he shares the same naming prefix as "Radagon", and this similarity is further emphasized with the "Rada" fruit placed throughout the DLC. Radagon is THE root catalyst below everything wrong in the Lands Between (expressed in being the final boss), and in a class far above any individual demigod for all of the threads that tie back to him. Prior to the DLC Godwyn had closure in the form of Fia's mending rune, and Godrick the Grafted/Golden basically being Godwyn-lite. Like, if Godwyn had been around he would have claimed the same rune as Godrick. About Radahn, it was known that he emulated Godfrey, which raises the question "Who is consort to Radahn as Godfrey is to Marika?". And why did he challenge the stars anyways? The DLC appears crafted to answer the questions about "why is Radahn?" that people didn't think to ask.

DLC plans are probably the reason why the devs cut the dreambrew quest. It suited an earlier version of Miquella's character that did not make sense anymore once the decision was made to have Miquella be taken by the Lord of Blood and corrupted. However, because there is extensive data mining for the game, just knowing that there was a dreambrew quest culminating in the "Miquella is St. Trina revelation" (and the theoretical "Age of Abundance and Decay" ending) was how Miquella was previously contextualized by fandom. Think about that: Miquella, the empyrean associated with sleep/dream, was understood through wishful thinking about content that was purposefully not implemented in the release version of the game.

Relying on cut content as secret canon is a slippery slope to disappointment, but in hindsight with full knowledge of the DLC the intention may be clearer. The player who looks backwards and inwards into the unused code of the game discovers a version of Miquella as he was. But since then the game version Miquella has undergone character development, by means of being taken by the Lord of Blood - as implemented with the cocoon of the empyrean. "Mohg, Lord of Blood" as a demigod character was also speculated as a late addition to the base game according to data miners who note that his AI is simply labelled "Greater Demon". Giving the impression that Miquella's diminished role in the base game narrative coincides with Mohg's increased status.

[mariacallous]

A nondescript suite of rooms in an office building in central Oslo hosts activity of the kind one would normally associate with a military headquarters or the CIA. In the central situation room, a monitor displays activity across the world’s oceans, while analysts at neighboring desks update this information around the clock. But this is not an intelligence agency  — it’s DNK, a Norwegian insurer of maritime war risk.

Today, intelligence is no longer just the domain of government agencies — or, rather, it shouldn’t be. Companies are now far more likely to be harmed by geopolitically linked events than ever before outside of full-scale wars. Thus, they need to keep a constant eye on the world. And if they do, they’re likely to see things that would be equally useful for their governments to know. 

DNK (a partner of the Atlantic Council’s Maritime Threats initiative, which I lead) has long insured merchant vessels against serious risks, and in today’s geopolitical climate, such risks are growing — fast. That means the company needs to know precisely what’s taking place in every corner of the maritime world, at all times.

“Intelligence are knowledge-based forecasts derived from verified data tailored to support decision-making. That’s different from what you get in the news,” said Freddy Furulund, who directs the Intelligence and Operations Center.

“To be able to provide such forecasts, you need to not only describe precisely what has happened, but you also need to contextualize it and describe its consequences for future voyages for the shipowner. We get data pointing in different directions, verify it, contextualize it and, most importantly, assess where it points to.” 

Some of this data comes from open sources, some from satellites and other technical means, and some from human sources. All of it is collected legally, and it all helps shipowners decide where to send their ships. “Intelligence providers are often seen as the bringer of bad news,” Furulund observed. “We inform our clients about how dangerous situations are in the days to come, but we also tell them about opportunities,” which can include things like minor changes to a ship’s route or placing armed guards on board.

Staffed by ex-military and intelligence professionals, the Intelligence and Operations Center has been operating since 2016, but intensifying political tensions over the past couple years have made it positively indispensable for both DNK and its clients. “Historically, the war risks insurance market argued that war risks would strike the insureds at random — which meant that loss-prevention activities, such as analysis of threat actors’ intentions and capabilities, weren’t pursued,” explained Svein Ringbakken, DNK’s managing director. But that’s changed dramatically.’

It’s not just that the Houthis are systematically targeting ships linked to Western countries; they also have far better weaponry than pirates ever did — and similar militias could start attacking merchant vessels elsewhere too. Indeed, some countries around the world appear to have decided to disregard global maritime rules altogether. And that makes it imperative for shipowners and insurers — not to mention crews — to understand where misfortune may strike from.

Meanwhile, foreign government officials visiting Norway often schedule a stop at DNK to see the center at work, and Furulund’s team regularly shares information with Western governments. “When we see something that poses an imminent threat to someone, we share that with not only the shipowner but also with the government — if it’s the government of Norway or a friendly country — or with whoever needs to be alerted,” he said. “It’s the ethical thing to do.”

Essentially, if Western countries are to withstand the aggression waged by various rivals and their proxies, sharing insights from the front line is the only way forward. And for Western countries today, it’s businesses rather than soldiers that are on the daily front line. (Should a war break out that would clearly change — but businesses will still face massive geopolitical risks.)

Not every company can operate its own intelligence center, of course, but more and more companies are now discovering they ought to collect threat information more systematically. Businesses simply need to do their best to discern what geopolitically linked risks may face them, not just in the Red Sea but in the Taiwan Strait, the South China Sea, the Baltic Sea, along the Cape of Good Hope route and other waters too — not to mention on land.

The Houthis recently resumed their campaign against Western shipping in the Red Sea after a couple weeks’ silence. Meanwhile, China’s maritime harassment of civilian vessels in the South China Sea continues. Is Beijing likely to expand its punishment of Western companies as proxies for their home countries? Will nations increasingly close to Russia and China try to harm Western companies operating in their countries? Western companies and governments need to know the answers to these questions — or at least gather enough information to make qualified assessments.

As many Western companies operate in places Western governments don’t, they should then share their insights with their home governments and other friendly states. And governments should return the favor.

We may not be able to clearly predict the acts of hostile states and groups with complete certainty, but making qualified assessments is far superior to sailing into uncharted waters. And that’s why we need a whole-of-society approach to intelligence too.

[aiseoexperteurope]

WHAT IS VERTEX AI SEARCH

Vertex AI Search: A Comprehensive Analysis

1. Executive Summary

Vertex AI Search emerges as a pivotal component of Google Cloud's artificial intelligence portfolio, offering enterprises the capability to deploy search experiences with the quality and sophistication characteristic of Google's own search technologies. This service is fundamentally designed to handle diverse data types, both structured and unstructured, and is increasingly distinguished by its deep integration with generative AI, most notably through its out-of-the-box Retrieval Augmented Generation (RAG) functionalities. This RAG capability is central to its value proposition, enabling organizations to ground large language model (LLM) responses in their proprietary data, thereby enhancing accuracy, reliability, and contextual relevance while mitigating the risk of generating factually incorrect information.

The platform's strengths are manifold, stemming from Google's decades of expertise in semantic search and natural language processing. Vertex AI Search simplifies the traditionally complex workflows associated with building RAG systems, including data ingestion, processing, embedding, and indexing. It offers specialized solutions tailored for key industries such as retail, media, and healthcare, addressing their unique vernacular and operational needs. Furthermore, its integration within the broader Vertex AI ecosystem, including access to advanced models like Gemini, positions it as a comprehensive solution for building sophisticated AI-driven applications.

However, the adoption of Vertex AI Search is not without its considerations. The pricing model, while granular and offering a "pay-as-you-go" approach, can be complex, necessitating careful cost modeling, particularly for features like generative AI and always-on components such as Vector Search index serving. User experiences and technical documentation also point to potential implementation hurdles for highly specific or advanced use cases, including complexities in IAM permission management and evolving query behaviors with platform updates. The rapid pace of innovation, while a strength, also requires organizations to remain adaptable.

Ultimately, Vertex AI Search represents a strategic asset for organizations aiming to unlock the value of their enterprise data through advanced search and AI. It provides a pathway to not only enhance information retrieval but also to build a new generation of AI-powered applications that are deeply informed by and integrated with an organization's unique knowledge base. Its continued evolution suggests a trajectory towards becoming a core reasoning engine for enterprise AI, extending beyond search to power more autonomous and intelligent systems.

2. Introduction to Vertex AI Search

Vertex AI Search is establishing itself as a significant offering within Google Cloud's AI capabilities, designed to transform how enterprises access and utilize their information. Its strategic placement within the Google Cloud ecosystem and its core value proposition address critical needs in the evolving landscape of enterprise data management and artificial intelligence.

Defining Vertex AI Search

Vertex AI Search is a service integrated into Google Cloud's Vertex AI Agent Builder. Its primary function is to equip developers with the tools to create secure, high-quality search experiences comparable to Google's own, tailored for a wide array of applications. These applications span public-facing websites, internal corporate intranets, and, significantly, serve as the foundation for Retrieval Augmented Generation (RAG) systems that power generative AI agents and applications. The service achieves this by amalgamating deep information retrieval techniques, advanced natural language processing (NLP), and the latest innovations in large language model (LLM) processing. This combination allows Vertex AI Search to more accurately understand user intent and deliver the most pertinent results, marking a departure from traditional keyword-based search towards more sophisticated semantic and conversational search paradigms.  

Strategic Position within Google Cloud AI Ecosystem

The service is not a standalone product but a core element of Vertex AI, Google Cloud's comprehensive and unified machine learning platform. This integration is crucial, as Vertex AI Search leverages and interoperates with other Vertex AI tools and services. Notable among these are Document AI, which facilitates the processing and understanding of diverse document formats , and direct access to Google's powerful foundation models, including the multimodal Gemini family. Its incorporation within the Vertex AI Agent Builder further underscores Google's strategy to provide an end-to-end toolkit for constructing advanced AI agents and applications, where robust search and retrieval capabilities are fundamental.  

Core Purpose and Value Proposition

The fundamental aim of Vertex AI Search is to empower enterprises to construct search applications of Google's caliber, operating over their own controlled datasets, which can encompass both structured and unstructured information. A central pillar of its value proposition is its capacity to function as an "out-of-the-box" RAG system. This feature is critical for grounding LLM responses in an enterprise's specific data, a process that significantly improves the accuracy, reliability, and contextual relevance of AI-generated content, thereby reducing the propensity for LLMs to produce "hallucinations" or factually incorrect statements. The simplification of the intricate workflows typically associated with RAG systems—including Extract, Transform, Load (ETL) processes, Optical Character Recognition (OCR), data chunking, embedding generation, and indexing—is a major attraction for businesses.  

Moreover, Vertex AI Search extends its utility through specialized, pre-tuned offerings designed for specific industries such as retail (Vertex AI Search for Commerce), media and entertainment (Vertex AI Search for Media), and healthcare and life sciences. These tailored solutions are engineered to address the unique terminologies, data structures, and operational requirements prevalent in these sectors.  

The pronounced emphasis on "out-of-the-box RAG" and the simplification of data processing pipelines points towards a deliberate strategy by Google to lower the entry barrier for enterprises seeking to leverage advanced Generative AI capabilities. Many organizations may lack the specialized AI talent or resources to build such systems from the ground up. Vertex AI Search offers a managed, pre-configured solution, effectively democratizing access to sophisticated RAG technology. By making these capabilities more accessible, Google is not merely selling a search product; it is positioning Vertex AI Search as a foundational layer for a new wave of enterprise AI applications. This approach encourages broader adoption of Generative AI within businesses by mitigating some inherent risks, like LLM hallucinations, and reducing technical complexities. This, in turn, is likely to drive increased consumption of other Google Cloud services, such as storage, compute, and LLM APIs, fostering a more integrated and potentially "sticky" ecosystem.  

Furthermore, Vertex AI Search serves as a conduit between traditional enterprise search mechanisms and the frontier of advanced AI. It is built upon "Google's deep expertise and decades of experience in semantic search technologies" , while concurrently incorporating "the latest in large language model (LLM) processing" and "Gemini generative AI". This dual nature allows it to support conventional search use cases, such as website and intranet search , alongside cutting-edge AI applications like RAG for generative AI agents and conversational AI systems. This design provides an evolutionary pathway for enterprises. Organizations can commence by enhancing existing search functionalities and then progressively adopt more advanced AI features as their internal AI maturity and comfort levels grow. This adaptability makes Vertex AI Search an attractive proposition for a diverse range of customers with varying immediate needs and long-term AI ambitions. Such an approach enables Google to capture market share in both the established enterprise search market and the rapidly expanding generative AI application platform market. It offers a smoother transition for businesses, diminishing the perceived risk of adopting state-of-the-art AI by building upon familiar search paradigms, thereby future-proofing their investment.  

3. Core Capabilities and Architecture

Vertex AI Search is engineered with a rich set of features and a flexible architecture designed to handle diverse enterprise data and power sophisticated search and AI applications. Its capabilities span from foundational search quality to advanced generative AI enablement, supported by robust data handling mechanisms and extensive customization options.

Key Features

Vertex AI Search integrates several core functionalities that define its power and versatility:

Google-Quality Search: At its heart, the service leverages Google's profound experience in semantic search technologies. This foundation aims to deliver highly relevant search results across a wide array of content types, moving beyond simple keyword matching to incorporate advanced natural language understanding (NLU) and contextual awareness.  

Out-of-the-Box Retrieval Augmented Generation (RAG): A cornerstone feature is its ability to simplify the traditionally complex RAG pipeline. Processes such as ETL, OCR, document chunking, embedding generation, indexing, storage, information retrieval, and summarization are streamlined, often requiring just a few clicks to configure. This capability is paramount for grounding LLM responses in enterprise-specific data, which significantly enhances the trustworthiness and accuracy of generative AI applications.  

Document Understanding: The service benefits from integration with Google's Document AI suite, enabling sophisticated processing of both structured and unstructured documents. This allows for the conversion of raw documents into actionable data, including capabilities like layout parsing and entity extraction.  

Vector Search: Vertex AI Search incorporates powerful vector search technology, essential for modern embeddings-based applications. While it offers out-of-the-box embedding generation and automatic fine-tuning, it also provides flexibility for advanced users. They can utilize custom embeddings and gain direct control over the underlying vector database for specialized use cases such as recommendation engines and ad serving. Recent enhancements include the ability to create and deploy indexes without writing code, and a significant reduction in indexing latency for smaller datasets, from hours down to minutes. However, it's important to note user feedback regarding Vector Search, which has highlighted concerns about operational costs (e.g., the need to keep compute resources active even when not querying), limitations with certain file types (e.g., .xlsx), and constraints on embedding dimensions for specific corpus configurations. This suggests a balance to be struck between the power of Vector Search and its operational overhead and flexibility.  

Generative AI Features: The platform is designed to enable grounded answers by synthesizing information from multiple sources. It also supports the development of conversational AI capabilities , often powered by advanced models like Google's Gemini.  

Comprehensive APIs: For developers who require fine-grained control or are building bespoke RAG solutions, Vertex AI Search exposes a suite of APIs. These include APIs for the Document AI Layout Parser, ranking algorithms, grounded generation, and the check grounding API, which verifies the factual basis of generated text.  

Data Handling

Effective data management is crucial for any search system. Vertex AI Search provides several mechanisms for ingesting, storing, and organizing data:

Supported Data Sources:

Websites: Content can be indexed by simply providing site URLs.  

Structured Data: The platform supports data from BigQuery tables and NDJSON files, enabling hybrid search (a combination of keyword and semantic search) or recommendation systems. Common examples include product catalogs, movie databases, or professional directories.  

Unstructured Data: Documents in various formats (PDF, DOCX, etc.) and images can be ingested for hybrid search. Use cases include searching through private repositories of research publications or financial reports. Notably, some limitations, such as lack of support for .xlsx files, have been reported specifically for Vector Search.  

Healthcare Data: FHIR R4 formatted data, often imported from the Cloud Healthcare API, can be used to enable hybrid search over clinical data and patient records.  

Media Data: A specialized structured data schema is available for the media industry, catering to content like videos, news articles, music tracks, and podcasts.  

Third-party Data Sources: Vertex AI Search offers connectors (some in Preview) to synchronize data from various third-party applications, such as Jira, Confluence, and Salesforce, ensuring that search results reflect the latest information from these systems.  

Data Stores and Apps: A fundamental architectural concept in Vertex AI Search is the one-to-one relationship between an "app" (which can be a search or a recommendations app) and a "data store". Data is imported into a specific data store, where it is subsequently indexed. The platform provides different types of data stores, each optimized for a particular kind of data (e.g., website content, structured data, unstructured documents, healthcare records, media assets).  

Indexing and Corpus: The term "corpus" refers to the underlying storage and indexing mechanism within Vertex AI Search. Even when users interact with data stores, which act as an abstraction layer, the corpus is the foundational component where data is stored and processed. It is important to understand that costs are associated with the corpus, primarily driven by the volume of indexed data, the amount of storage consumed, and the number of queries processed.  

Schema Definition: Users have the ability to define a schema that specifies which metadata fields from their documents should be indexed. This schema also helps in understanding the structure of the indexed documents.  

Real-time Ingestion: For datasets that change frequently, Vertex AI Search supports real-time ingestion. This can be implemented using a Pub/Sub topic to publish notifications about new or updated documents. A Cloud Function can then subscribe to this topic and use the Vertex AI Search API to ingest, update, or delete documents in the corresponding data store, thereby maintaining data freshness. This is a critical feature for dynamic environments.  

Automated Processing for RAG: When used for Retrieval Augmented Generation, Vertex AI Search automates many of the complex data processing steps, including ETL, OCR, document chunking, embedding generation, and indexing.  

The "corpus" serves as the foundational layer for both storage and indexing, and its management has direct cost implications. While data stores provide a user-friendly abstraction, the actual costs are tied to the size of this underlying corpus and the activity it handles. This means that effective data management strategies, such as determining what data to index and defining retention policies, are crucial for optimizing costs, even with the simplified interface of data stores. The "pay only for what you use" principle is directly linked to the activity and volume within this corpus. For large-scale deployments, particularly those involving substantial datasets like the 500GB use case mentioned by a user , the cost implications of the corpus can be a significant planning factor.  

There is an observable interplay between the platform's "out-of-the-box" simplicity and the requirements of advanced customization. Vertex AI Search is heavily promoted for its ease of setup and pre-built RAG capabilities , with an emphasis on an "easy experience to get started". However, highly specific enterprise scenarios or complex user requirements—such as querying by unique document identifiers, maintaining multi-year conversational contexts, needing specific embedding dimensions, or handling unsupported file formats like XLSX —may necessitate delving into more intricate configurations, API utilization, and custom development work. For example, implementing real-time ingestion requires setting up Pub/Sub and Cloud Functions , and achieving certain filtering behaviors might involve workarounds like using metadata fields. While comprehensive APIs are available for "granular control or bespoke RAG solutions" , this means that the platform's inherent simplicity has boundaries, and deep technical expertise might still be essential for optimal or highly tailored implementations. This suggests a tiered user base: one that leverages Vertex AI Search as a turnkey solution, and another that uses it as a powerful, extensible toolkit for custom builds.  

Querying and Customization

Vertex AI Search provides flexible ways to query data and customize the search experience:

Query Types: The platform supports Google-quality search, which represents an evolution from basic keyword matching to modern, conversational search experiences. It can be configured to return only a list of search results or to provide generative, AI-powered answers. A recent user-reported issue (May 2025) indicated that queries against JSON data in the latest release might require phrasing in natural language, suggesting an evolving query interpretation mechanism that prioritizes NLU.  

Customization Options:

Vertex AI Search offers extensive capabilities to tailor search experiences to specific needs.  

Metadata Filtering: A key customization feature is the ability to filter search results based on indexed metadata fields. For instance, if direct filtering by rag_file_ids is not supported by a particular API (like the Grounding API), adding a file_id to document metadata and filtering on that field can serve as an effective alternative.  

Search Widget: Integration into websites can be achieved easily by embedding a JavaScript widget or an HTML component.  

API Integration: For more profound control and custom integrations, the AI Applications API can be used.  

LLM Feature Activation: Features that provide generative answers powered by LLMs typically need to be explicitly enabled.  

Refinement Options: Users can preview search results and refine them by adding or modifying metadata (e.g., based on HTML structure for websites), boosting the ranking of certain results (e.g., based on publication date), or applying filters (e.g., based on URL patterns or other metadata).  

Events-based Reranking and Autocomplete: The platform also supports advanced tuning options such as reranking results based on user interaction events and providing autocomplete suggestions for search queries.  

Multi-Turn Conversation Support:

For conversational AI applications, the Grounding API can utilize the history of a conversation as context for generating subsequent responses.  

To maintain context in multi-turn dialogues, it is recommended to store previous prompts and responses (e.g., in a database or cache) and include this history in the next prompt to the model, while being mindful of the context window limitations of the underlying LLMs.  

The evolving nature of query interpretation, particularly the reported shift towards requiring natural language queries for JSON data , underscores a broader trend. If this change is indicative of a deliberate platform direction, it signals a significant alignment of the query experience with Google's core strengths in NLU and conversational AI, likely driven by models like Gemini. This could simplify interactions for end-users but may require developers accustomed to more structured query languages for structured data to adapt their approaches. Such a shift prioritizes natural language understanding across the platform. However, it could also introduce friction for existing applications or development teams that have built systems based on previous query behaviors. This highlights the dynamic nature of managed services, where underlying changes can impact functionality, necessitating user adaptation and diligent monitoring of release notes.  

4. Applications and Use Cases

Vertex AI Search is designed to cater to a wide spectrum of applications, from enhancing traditional enterprise search to enabling sophisticated generative AI solutions across various industries. Its versatility allows organizations to leverage their data in novel and impactful ways.

Enterprise Search

A primary application of Vertex AI Search is the modernization and improvement of search functionalities within an organization:

Improving Search for Websites and Intranets: The platform empowers businesses to deploy Google-quality search capabilities on their external-facing websites and internal corporate portals or intranets. This can significantly enhance user experience by making information more discoverable. For basic implementations, this can be as straightforward as integrating a pre-built search widget.  

Employee and Customer Search: Vertex AI Search provides a comprehensive toolkit for accessing, processing, and analyzing enterprise information. This can be used to create powerful search experiences for employees, helping them find internal documents, locate subject matter experts, or access company knowledge bases more efficiently. Similarly, it can improve customer-facing search for product discovery, support documentation, or FAQs.  

Generative AI Enablement

Vertex AI Search plays a crucial role in the burgeoning field of generative AI by providing essential grounding capabilities:

Grounding LLM Responses (RAG): A key and frequently highlighted use case is its function as an out-of-the-box Retrieval Augmented Generation (RAG) system. In this capacity, Vertex AI Search retrieves relevant and factual information from an organization's own data repositories. This retrieved information is then used to "ground" the responses generated by Large Language Models (LLMs). This process is vital for improving the accuracy, reliability, and contextual relevance of LLM outputs, and critically, for reducing the incidence of "hallucinations"—the tendency of LLMs to generate plausible but incorrect or fabricated information.  

Powering Generative AI Agents and Apps: By providing robust grounding capabilities, Vertex AI Search serves as a foundational component for building sophisticated generative AI agents and applications. These AI systems can then interact with and reason about company-specific data, leading to more intelligent and context-aware automated solutions.  

Industry-Specific Solutions

Recognizing that different industries have unique data types, terminologies, and objectives, Google Cloud offers specialized versions of Vertex AI Search:

Vertex AI Search for Commerce (Retail): This version is specifically tuned to enhance the search, product recommendation, and browsing experiences on retail e-commerce channels. It employs AI to understand complex customer queries, interpret shopper intent (even when expressed using informal language or colloquialisms), and automatically provide dynamic spell correction and relevant synonym suggestions. Furthermore, it can optimize search results based on specific business objectives, such as click-through rates (CTR), revenue per session, and conversion rates.  

Vertex AI Search for Media (Media and Entertainment): Tailored for the media industry, this solution aims to deliver more personalized content recommendations, often powered by generative AI. The strategic goal is to increase consumer engagement and time spent on media platforms, which can translate to higher advertising revenue, subscription retention, and overall platform loyalty. It supports structured data formats commonly used in the media sector for assets like videos, news articles, music, and podcasts.  

Vertex AI Search for Healthcare and Life Sciences: This offering provides a medically tuned search engine designed to improve the experiences of both patients and healthcare providers. It can be used, for example, to search through vast clinical data repositories, electronic health records, or a patient's clinical history using exploratory queries. This solution is also built with compliance with healthcare data regulations like HIPAA in mind.  

The development of these industry-specific versions like "Vertex AI Search for Commerce," "Vertex AI Search for Media," and "Vertex AI Search for Healthcare and Life Sciences" is not merely a cosmetic adaptation. It represents a strategic decision by Google to avoid a one-size-fits-all approach. These offerings are "tuned for unique industry requirements" , incorporating specialized terminologies, understanding industry-specific data structures, and aligning with distinct business objectives. This targeted approach significantly lowers the barrier to adoption for companies within these verticals, as the solution arrives pre-optimized for their particular needs, thereby reducing the requirement for extensive custom development or fine-tuning. This industry-specific strategy serves as a potent market penetration tactic, allowing Google to compete more effectively against niche players in each vertical and to demonstrate clear return on investment by addressing specific, high-value industry challenges. It also fosters deeper integration into the core business processes of these enterprises, positioning Vertex AI Search as a more strategic and less easily substitutable component of their technology infrastructure. This could, over time, lead to the development of distinct, industry-focused data ecosystems and best practices centered around Vertex AI Search.  

Embeddings-Based Applications (via Vector Search)

The underlying Vector Search capability within Vertex AI Search also enables a range of applications that rely on semantic similarity of embeddings:

Recommendation Engines: Vector Search can be a core component in building recommendation engines. By generating numerical representations (embeddings) of items (e.g., products, articles, videos), it can find and suggest items that are semantically similar to what a user is currently viewing or has interacted with in the past.  

Chatbots: For advanced chatbots that need to understand user intent deeply and retrieve relevant information from extensive knowledge bases, Vector Search provides powerful semantic matching capabilities. This allows chatbots to provide more accurate and contextually appropriate responses.  

Ad Serving: In the domain of digital advertising, Vector Search can be employed for semantic matching to deliver more relevant advertisements to users based on content or user profiles.  

The Vector Search component is presented both as an integral technology powering the semantic retrieval within the managed Vertex AI Search service and as a potent, standalone tool accessible via the broader Vertex AI platform. Snippet , for instance, outlines a methodology for constructing a recommendation engine using Vector Search directly. This dual role means that Vector Search is foundational to the core semantic retrieval capabilities of Vertex AI Search, and simultaneously, it is a powerful component that can be independently leveraged by developers to build other custom AI applications. Consequently, enhancements to Vector Search, such as the recently reported reductions in indexing latency , benefit not only the out-of-the-box Vertex AI Search experience but also any custom AI solutions that developers might construct using this underlying technology. Google is, in essence, offering a spectrum of access to its vector database technology. Enterprises can consume it indirectly and with ease through the managed Vertex AI Search offering, or they can harness it more directly for bespoke AI projects. This flexibility caters to varying levels of technical expertise and diverse application requirements. As more enterprises adopt embeddings for a multitude of AI tasks, a robust, scalable, and user-friendly Vector Search becomes an increasingly critical piece of infrastructure, likely driving further adoption of the entire Vertex AI ecosystem.  

Document Processing and Analysis

Leveraging its integration with Document AI, Vertex AI Search offers significant capabilities in document processing:

The service can help extract valuable information, classify documents based on content, and split large documents into manageable chunks. This transforms static documents into actionable intelligence, which can streamline various business workflows and enable more data-driven decision-making. For example, it can be used for analyzing large volumes of textual data, such as customer feedback, product reviews, or research papers, to extract key themes and insights.  

Case Studies (Illustrative Examples)

While specific case studies for "Vertex AI Search" are sometimes intertwined with broader "Vertex AI" successes, several examples illustrate the potential impact of AI grounded on enterprise data, a core principle of Vertex AI Search:

Genial Care (Healthcare): This organization implemented Vertex AI to improve the process of keeping session records for caregivers. This enhancement significantly aided in reviewing progress for autism care, demonstrating Vertex AI's value in managing and utilizing healthcare-related data.  

AES (Manufacturing & Industrial): AES utilized generative AI agents, built with Vertex AI, to streamline energy safety audits. This application resulted in a remarkable 99% reduction in costs and a decrease in audit completion time from 14 days to just one hour. This case highlights the transformative potential of AI agents that are effectively grounded on enterprise-specific information, aligning closely with the RAG capabilities central to Vertex AI Search.  

Xometry (Manufacturing): This company is reported to be revolutionizing custom manufacturing processes by leveraging Vertex AI.  

LUXGEN (Automotive): LUXGEN employed Vertex AI to develop an AI-powered chatbot. This initiative led to improvements in both the car purchasing and driving experiences for customers, while also achieving a 30% reduction in customer service workloads.  

These examples, though some may refer to the broader Vertex AI platform, underscore the types of business outcomes achievable when AI is effectively applied to enterprise data and processes—a domain where Vertex AI Search is designed to excel.

5. Implementation and Management Considerations

Successfully deploying and managing Vertex AI Search involves understanding its setup processes, data ingestion mechanisms, security features, and user access controls. These aspects are critical for ensuring the platform operates efficiently, securely, and in alignment with enterprise requirements.

Setup and Deployment

Vertex AI Search offers flexibility in how it can be implemented and integrated into existing systems:

Google Cloud Console vs. API: Implementation can be approached in two main ways. The Google Cloud console provides a web-based interface for a quick-start experience, allowing users to create applications, import data, test search functionality, and view analytics without extensive coding. Alternatively, for deeper integration into websites or custom applications, the AI Applications API offers programmatic control. A common practice is a hybrid approach, where initial setup and data management are performed via the console, while integration and querying are handled through the API.  

App and Data Store Creation: The typical workflow begins with creating a search or recommendations "app" and then attaching it to a "data store." Data relevant to the application is then imported into this data store and subsequently indexed to make it searchable.  

Embedding JavaScript Widgets: For straightforward website integration, Vertex AI Search provides embeddable JavaScript widgets and API samples. These allow developers to quickly add search or recommendation functionalities to their web pages as HTML components.  

Data Ingestion and Management

The platform provides robust mechanisms for ingesting data from various sources and keeping it up-to-date:

Corpus Management: As previously noted, the "corpus" is the fundamental underlying storage and indexing layer. While data stores offer an abstraction, it is crucial to understand that costs are directly related to the volume of data indexed in the corpus, the storage it consumes, and the query load it handles.  

Pub/Sub for Real-time Updates: For environments with dynamic datasets where information changes frequently, Vertex AI Search supports real-time updates. This is typically achieved by setting up a Pub/Sub topic to which notifications about new or modified documents are published. A Cloud Function, acting as a subscriber to this topic, can then use the Vertex AI Search API to ingest, update, or delete the corresponding documents in the data store. This architecture ensures that the search index remains fresh and reflects the latest information. The capacity for real-time ingestion via Pub/Sub and Cloud Functions is a significant feature. This capability distinguishes it from systems reliant solely on batch indexing, which may not be adequate for environments with rapidly changing information. Real-time ingestion is vital for use cases where data freshness is paramount, such as e-commerce platforms with frequently updated product inventories, news portals, live financial data feeds, or internal systems tracking real-time operational metrics. Without this, search results could quickly become stale and potentially misleading. This feature substantially broadens the applicability of Vertex AI Search, positioning it as a viable solution for dynamic, operational systems where search must accurately reflect the current state of data. However, implementing this real-time pipeline introduces additional architectural components (Pub/Sub topics, Cloud Functions) and associated costs, which organizations must consider in their planning. It also implies a need for robust monitoring of the ingestion pipeline to ensure its reliability.  

Metadata for Filtering and Control: During the schema definition process, specific metadata fields can be designated for indexing. This indexed metadata is critical for enabling powerful filtering of search results. For example, if an application requires users to search within a specific subset of documents identified by a unique ID, and direct filtering by a system-generated rag_file_id is not supported in a particular API context, a workaround involves adding a custom file_id field to each document's metadata. This custom field can then be used as a filter criterion during search queries.  

Data Connectors: To facilitate the ingestion of data from a variety of sources, including first-party systems, other Google services, and third-party applications (such as Jira, Confluence, and Salesforce), Vertex AI Search offers data connectors. These connectors provide read-only access to external applications and help ensure that the data within the search index remains current and synchronized with these source systems.  

Security and Compliance

Google Cloud places a strong emphasis on security and compliance for its services, and Vertex AI Search incorporates several features to address these enterprise needs:

Data Privacy: A core tenet is that user data ingested into Vertex AI Search is secured within the customer's dedicated cloud instance. Google explicitly states that it does not access or use this customer data for training its general-purpose models or for any other unauthorized purposes.  

Industry Compliance: Vertex AI Search is designed to adhere to various recognized industry standards and regulations. These include HIPAA (Health Insurance Portability and Accountability Act) for healthcare data, the ISO 27000-series for information security management, and SOC (System and Organization Controls) attestations (SOC-1, SOC-2, SOC-3). This compliance is particularly relevant for the specialized versions of Vertex AI Search, such as the one for Healthcare and Life Sciences.  

Access Transparency: This feature, when enabled, provides customers with logs of actions taken by Google personnel if they access customer systems (typically for support purposes), offering a degree of visibility into such interactions.  

Virtual Private Cloud (VPC) Service Controls: To enhance data security and prevent unauthorized data exfiltration or infiltration, customers can use VPC Service Controls to define security perimeters around their Google Cloud resources, including Vertex AI Search.  

Customer-Managed Encryption Keys (CMEK): Available in Preview, CMEK allows customers to use their own cryptographic keys (managed through Cloud Key Management Service) to encrypt data at rest within Vertex AI Search. This gives organizations greater control over their data's encryption.  

User Access and Permissions (IAM)

Proper configuration of Identity and Access Management (IAM) permissions is fundamental to securing Vertex AI Search and ensuring that users only have access to appropriate data and functionalities:

Effective IAM policies are critical. However, some users have reported encountering challenges when trying to identify and configure the specific "Discovery Engine search permissions" required for Vertex AI Search. Difficulties have been noted in determining factors such as principal access boundaries or the impact of deny policies, even when utilizing tools like the IAM Policy Troubleshooter. This suggests that the permission model can be granular and may require careful attention to detail and potentially specialized knowledge to implement correctly, especially for complex scenarios involving fine-grained access control.  

The power of Vertex AI Search lies in its capacity to index and make searchable vast quantities of potentially sensitive enterprise data drawn from diverse sources. While Google Cloud provides a robust suite of security features like VPC Service Controls and CMEK , the responsibility for meticulous IAM configuration and overarching data governance rests heavily with the customer. The user-reported difficulties in navigating IAM permissions for "Discovery Engine search permissions" underscore that the permission model, while offering granular control, might also present complexity. Implementing a least-privilege access model effectively, especially when dealing with nuanced requirements such as filtering search results based on user identity or specific document IDs , may require specialized expertise. Failure to establish and maintain correct IAM policies could inadvertently lead to security vulnerabilities or compliance breaches, thereby undermining the very benefits the search platform aims to provide. Consequently, the "ease of use" often highlighted for search setup must be counterbalanced with rigorous and continuous attention to security and access control from the outset of any deployment. The platform's capability to filter search results based on metadata becomes not just a functional feature but a key security control point if designed and implemented with security considerations in mind.  

6. Pricing and Commercials

Understanding the pricing structure of Vertex AI Search is essential for organizations evaluating its adoption and for ongoing cost management. The model is designed around the principle of "pay only for what you use" , offering flexibility but also requiring careful consideration of various cost components. Google Cloud typically provides a free trial, often including $300 in credits for new customers to explore services. Additionally, a free tier is available for some services, notably a 10 GiB per month free quota for Index Data Storage, which is shared across AI Applications.  

The pricing for Vertex AI Search can be broken down into several key areas:

Core Search Editions and Query Costs

Search Standard Edition: This edition is priced based on the number of queries processed, typically per 1,000 queries. For example, a common rate is $1.50 per 1,000 queries.  

Search Enterprise Edition: This edition includes Core Generative Answers (AI Mode) and is priced at a higher rate per 1,000 queries, such as $4.00 per 1,000 queries.  

Advanced Generative Answers (AI Mode): This is an optional add-on available for both Standard and Enterprise Editions. It incurs an additional cost per 1,000 user input queries, for instance, an extra $4.00 per 1,000 user input queries.  

Data Indexing Costs

Index Storage: Costs for storing indexed data are charged per GiB of raw data per month. A typical rate is $5.00 per GiB per month. As mentioned, a free quota (e.g., 10 GiB per month) is usually provided. This cost is directly associated with the underlying "corpus" where data is stored and managed.  

Grounding and Generative AI Cost Components

When utilizing the generative AI capabilities, particularly for grounding LLM responses, several components contribute to the overall cost :  

Input Prompt (for grounding): The cost is determined by the number of characters in the input prompt provided for the grounding process, including any grounding facts. An example rate is $0.000125 per 1,000 characters.

Output (generated by model): The cost for the output generated by the LLM is also based on character count. An example rate is $0.000375 per 1,000 characters.

Grounded Generation (for grounding on own retrieved data): There is a cost per 1,000 requests for utilizing the grounding functionality itself, for example, $2.50 per 1,000 requests.

Data Retrieval (Vertex AI Search - Enterprise edition): When Vertex AI Search (Enterprise edition) is used to retrieve documents for grounding, a query cost applies, such as $4.00 per 1,000 requests.

Check Grounding API: This API allows users to assess how well a piece of text (an answer candidate) is grounded in a given set of reference texts (facts). The cost is per 1,000 answer characters, for instance, $0.00075 per 1,000 answer characters.  

Industry-Specific Pricing

Vertex AI Search offers specialized pricing for its industry-tailored solutions:

Vertex AI Search for Healthcare: This version has a distinct, typically higher, query cost, such as $20.00 per 1,000 queries. It includes features like GenAI-powered answers and streaming updates to the index, some of which may be in Preview status. Data indexing costs are generally expected to align with standard rates.  

Vertex AI Search for Media:

Media Search API Request Count: A specific query cost applies, for example, $2.00 per 1,000 queries.  

Data Index: Standard data indexing rates, such as $5.00 per GB per month, typically apply.  

Media Recommendations: Pricing for media recommendations is often tiered based on the volume of prediction requests per month (e.g., $0.27 per 1,000 predictions for up to 20 million, $0.18 for the next 280 million, and so on). Additionally, training and tuning of recommendation models are charged per node per hour, for example, $2.50 per node per hour.  

Document AI Feature Pricing (when integrated)

If Vertex AI Search utilizes integrated Document AI features for processing documents, these will incur their own costs:

Enterprise Document OCR Processor: Pricing is typically tiered based on the number of pages processed per month, for example, $1.50 per 1,000 pages for 1 to 5 million pages per month.  

Layout Parser (includes initial chunking): This feature is priced per 1,000 pages, for instance, $10.00 per 1,000 pages.  

Vector Search Cost Considerations

Specific cost considerations apply to Vertex AI Vector Search, particularly highlighted by user feedback :  

A user found Vector Search to be "costly" due to the necessity of keeping compute resources (machines) continuously running for index serving, even during periods of no query activity. This implies ongoing costs for provisioned resources, distinct from per-query charges.  

Supporting documentation confirms this model, with "Index Serving" costs that vary by machine type and region, and "Index Building" costs, such as $3.00 per GiB of data processed.  

Pricing Examples

Illustrative pricing examples provided in sources like and demonstrate how these various components can combine to form the total cost for different usage scenarios, including general availability (GA) search functionality, media recommendations, and grounding operations.  

The following table summarizes key pricing components for Vertex AI Search:

Vertex AI Search Pricing SummaryService ComponentEdition/TypeUnitPrice (Example)Free Tier/NotesSearch QueriesStandard1,000 queries$1.5010k free trial queries often includedSearch QueriesEnterprise (with Core GenAI)1,000 queries$4.0010k free trial queries often includedAdvanced GenAI (Add-on)Standard or Enterprise1,000 user input queries+$4.00Index Data StorageAllGiB/month$5.0010 GiB/month free (shared across AI Applications)Grounding: Input PromptGenerative AI1,000 characters$0.000125Grounding: OutputGenerative AI1,000 characters$0.000375Grounding: Grounded GenerationGenerative AI1,000 requests$2.50For grounding on own retrieved dataGrounding: Data RetrievalEnterprise Search1,000 requests$4.00When using Vertex AI Search (Enterprise) for retrievalCheck Grounding APIAPI1,000 answer characters$0.00075Healthcare Search QueriesHealthcare1,000 queries$20.00Includes some Preview featuresMedia Search API QueriesMedia1,000 queries$2.00Media Recommendations (Predictions)Media1,000 predictions$0.27 (up to 20M/mo), $0.18 (next 280M/mo), $0.10 (after 300M/mo)Tiered pricingMedia Recs Training/TuningMediaNode/hour$2.50Document OCRDocument AI Integration1,000 pages$1.50 (1-5M pages/mo), $0.60 (>5M pages/mo)Tiered pricingLayout ParserDocument AI Integration1,000 pages$10.00Includes initial chunkingVector Search: Index BuildingVector SearchGiB processed$3.00Vector Search: Index ServingVector SearchVariesVaries by machine type & region (e.g., $0.094/node hour for e2-standard-2 in us-central1)Implies "always-on" costs for provisioned resourcesExport to Sheets

Note: Prices are illustrative examples based on provided research and are subject to change. Refer to official Google Cloud pricing documentation for current rates.

The multifaceted pricing structure, with costs broken down by queries, data volume, character counts for generative AI, specific APIs, and even underlying Document AI processors , reflects the feature richness and granularity of Vertex AI Search. This allows users to align costs with the specific features they consume, consistent with the "pay only for what you use" philosophy. However, this granularity also means that accurately estimating total costs can be a complex undertaking. Users must thoroughly understand their anticipated usage patterns across various dimensions—query volume, data size, frequency of generative AI interactions, document processing needs—to predict expenses with reasonable accuracy. The seemingly simple act of obtaining a generative answer, for instance, can involve multiple cost components: input prompt processing, output generation, the grounding operation itself, and the data retrieval query. Organizations, particularly those with large datasets, high query volumes, or plans for extensive use of generative features, may find it challenging to forecast costs without detailed analysis and potentially leveraging tools like the Google Cloud pricing calculator. This complexity could present a barrier for smaller organizations or those with less experience in managing cloud expenditures. It also underscores the importance of closely monitoring usage to prevent unexpected costs. The decision between Standard and Enterprise editions, and whether to incorporate Advanced Generative Answers, becomes a significant cost-benefit analysis.  

Furthermore, a critical aspect of the pricing model for certain high-performance features like Vertex AI Vector Search is the "always-on" cost component. User feedback explicitly noted Vector Search as "costly" due to the requirement to "keep my machine on even when a user ain't querying". This is corroborated by pricing details that list "Index Serving" costs varying by machine type and region , which are distinct from purely consumption-based fees (like per-query charges) where costs would be zero if there were no activity. For features like Vector Search that necessitate provisioned infrastructure for index serving, a baseline operational cost exists regardless of query volume. This is a crucial distinction from on-demand pricing models and can significantly impact the total cost of ownership (TCO) for use cases that rely heavily on Vector Search but may experience intermittent query patterns. This continuous cost for certain features means that organizations must evaluate the ongoing value derived against their persistent expense. It might render Vector Search less economical for applications with very sporadic usage unless the benefits during active periods are substantial. This could also suggest that Google might, in the future, offer different tiers or configurations for Vector Search to cater to varying performance and cost needs, or users might need to architect solutions to de-provision and re-provision indexes if usage is highly predictable and infrequent, though this would add operational complexity.  

7. Comparative Analysis

Vertex AI Search operates in a competitive landscape of enterprise search and AI platforms. Understanding its position relative to alternatives is crucial for informed decision-making. Key comparisons include specialized product discovery solutions like Algolia and broader enterprise search platforms from other major cloud providers and niche vendors.

Vertex AI Search for Commerce vs. Algolia

For e-commerce and retail product discovery, Vertex AI Search for Commerce and Algolia are prominent solutions, each with distinct strengths :  

Core Search Quality & Features:

Vertex AI Search for Commerce is built upon Google's extensive search algorithm expertise, enabling it to excel at interpreting complex queries by understanding user context, intent, and even informal language. It features dynamic spell correction and synonym suggestions, consistently delivering high-quality, context-rich results. Its primary strengths lie in natural language understanding (NLU) and dynamic AI-driven corrections.

Algolia has established its reputation with a strong focus on semantic search and autocomplete functionalities, powered by its NeuralSearch capabilities. It adapts quickly to user intent. However, it may require more manual fine-tuning to address highly complex or context-rich queries effectively. Algolia is often prized for its speed, ease of configuration, and feature-rich autocomplete.

Customer Engagement & Personalization:

Vertex AI incorporates advanced recommendation models that adapt based on user interactions. It can optimize search results based on defined business objectives like click-through rates (CTR), revenue per session, and conversion rates. Its dynamic personalization capabilities mean search results evolve based on prior user behavior, making the browsing experience progressively more relevant. The deep integration of AI facilitates a more seamless, data-driven personalization experience.

Algolia offers an impressive suite of personalization tools with various recommendation models suitable for different retail scenarios. The platform allows businesses to customize search outcomes through configuration, aligning product listings, faceting, and autocomplete suggestions with their customer engagement strategy. However, its personalization features might require businesses to integrate additional services or perform more fine-tuning to achieve the level of dynamic personalization seen in Vertex AI.

Merchandising & Display Flexibility:

Vertex AI utilizes extensive AI models to enable dynamic ranking configurations that consider not only search relevance but also business performance metrics such as profitability and conversion data. The search engine automatically sorts products by match quality and considers which products are likely to drive the best business outcomes, reducing the burden on retail teams by continuously optimizing based on live data. It can also blend search results with curated collections and themes. A noted current limitation is that Google is still developing new merchandising tools, and the existing toolset is described as "fairly limited".  

Algolia offers powerful faceting and grouping capabilities, allowing for the creation of curated displays for promotions, seasonal events, or special collections. Its flexible configuration options permit merchants to manually define boost and slotting rules to prioritize specific products for better visibility. These manual controls, however, might require more ongoing maintenance compared to Vertex AI's automated, outcome-based ranking. Algolia's configuration-centric approach may be better suited for businesses that prefer hands-on control over merchandising details.

Implementation, Integration & Operational Efficiency:

A key advantage of Vertex AI is its seamless integration within the broader Google Cloud ecosystem, making it a natural choice for retailers already utilizing Google Merchant Center, Google Cloud Storage, or BigQuery. Its sophisticated AI models mean that even a simple initial setup can yield high-quality results, with the system automatically learning from user interactions over time. A potential limitation is its significant data requirements; businesses lacking large volumes of product or interaction data might not fully leverage its advanced capabilities, and smaller brands may find themselves in lower Data Quality tiers.  

Algolia is renowned for its ease of use and rapid deployment, offering a user-friendly interface, comprehensive documentation, and a free tier suitable for early-stage projects. It is designed to integrate with various e-commerce systems and provides a flexible API for straightforward customization. While simpler and more accessible for smaller businesses, this ease of use might necessitate additional configuration for very complex or data-intensive scenarios.

Analytics, Measurement & Future Innovations:

Vertex AI provides extensive insights into both search performance and business outcomes, tracking metrics like CTR, conversion rates, and profitability. The ability to export search and event data to BigQuery enhances its analytical power, offering possibilities for custom dashboards and deeper AI/ML insights. It is well-positioned to benefit from Google's ongoing investments in AI, integration with services like Google Vision API, and the evolution of large language models and conversational commerce.

Algolia offers detailed reporting on search performance, tracking visits, searches, clicks, and conversions, and includes views for data quality monitoring. Its analytics capabilities tend to focus more on immediate search performance rather than deeper business performance metrics like average order value or revenue impact. Algolia is also rapidly innovating, especially in enhancing its semantic search and autocomplete functions, though its evolution may be more incremental compared to Vertex AI's broader ecosystem integration.

In summary, Vertex AI Search for Commerce is often an ideal choice for large retailers with extensive datasets, particularly those already integrated into the Google or Shopify ecosystems, who are seeking advanced AI-driven optimization for customer engagement and business outcomes. Conversely, Algolia presents a strong option for businesses that prioritize rapid deployment, ease of use, and flexible semantic search and autocomplete functionalities, especially smaller retailers or those desiring more hands-on control over their search configuration.

Vertex AI Search vs. Other Enterprise Search Solutions

Beyond e-commerce, Vertex AI Search competes with a range of enterprise search solutions :  

INDICA Enterprise Search: This solution utilizes a patented approach to index both structured and unstructured data, prioritizing results by relevance. It offers a sophisticated query builder and comprehensive filtering options. Both Vertex AI Search and INDICA Enterprise Search provide API access, free trials/versions, and similar deployment and support options. INDICA lists "Sensitive Data Discovery" as a feature, while Vertex AI Search highlights "eCommerce Search, Retrieval-Augmented Generation (RAG), Semantic Search, and Site Search" as additional capabilities. Both platforms integrate with services like Gemini, Google Cloud Document AI, Google Cloud Platform, HTML, and Vertex AI.  

Azure AI Search: Microsoft's offering features a vector database specifically designed for advanced RAG and contemporary search functionalities. It emphasizes enterprise readiness, incorporating security, compliance, and ethical AI methodologies. Azure AI Search supports advanced retrieval techniques, integrates with various platforms and data sources, and offers comprehensive vector data processing (extraction, chunking, enrichment, vectorization). It supports diverse vector types, hybrid models, multilingual capabilities, metadata filtering, and extends beyond simple vector searches to include keyword match scoring, reranking, geospatial search, and autocomplete features. The strong emphasis on RAG and vector capabilities by both Vertex AI Search and Azure AI Search positions them as direct competitors in the AI-powered enterprise search market.  

IBM Watson Discovery: This platform leverages AI-driven search to extract precise answers and identify trends from various documents and websites. It employs advanced NLP to comprehend industry-specific terminology, aiming to reduce research time significantly by contextualizing responses and citing source documents. Watson Discovery also uses machine learning to visually categorize text, tables, and images. Its focus on deep NLP and understanding industry-specific language mirrors claims made by Vertex AI, though Watson Discovery has a longer established presence in this particular enterprise AI niche.  

Guru: An AI search and knowledge platform, Guru delivers trusted information from a company's scattered documents, applications, and chat platforms directly within users' existing workflows. It features a personalized AI assistant and can serve as a modern replacement for legacy wikis and intranets. Guru offers extensive native integrations with popular business tools like Slack, Google Workspace, Microsoft 365, Salesforce, and Atlassian products. Guru's primary focus on knowledge management and in-app assistance targets a potentially more specialized use case than the broader enterprise search capabilities of Vertex AI, though there is an overlap in accessing and utilizing internal knowledge.  

AddSearch: Provides fast, customizable site search for websites and web applications, using a crawler or an Indexing API. It offers enterprise-level features such as autocomplete, synonyms, ranking tools, and progressive ranking, designed to scale from small businesses to large corporations.  

Haystack: Aims to connect employees with the people, resources, and information they need. It offers intranet-like functionalities, including custom branding, a modular layout, multi-channel content delivery, analytics, knowledge sharing features, and rich employee profiles with a company directory.  

Atolio: An AI-powered enterprise search engine designed to keep data securely within the customer's own cloud environment (AWS, Azure, or GCP). It provides intelligent, permission-based responses and ensures that intellectual property remains under control, with LLMs that do not train on customer data. Atolio integrates with tools like Office 365, Google Workspace, Slack, and Salesforce. A direct comparison indicates that both Atolio and Vertex AI Search offer similar deployment, support, and training options, and share core features like AI/ML, faceted search, and full-text search. Vertex AI Search additionally lists RAG, Semantic Search, and Site Search as features not specified for Atolio in that comparison.  

The following table provides a high-level feature comparison:

Feature and Capability Comparison: Vertex AI Search vs. Key CompetitorsFeature/CapabilityVertex AI SearchAlgolia (Commerce)Azure AI SearchIBM Watson DiscoveryINDICA ESGuruAtolioPrimary FocusEnterprise Search + RAG, Industry SolutionsProduct Discovery, E-commerce SearchEnterprise Search + RAG, Vector DBNLP-driven Insight Extraction, Document AnalysisGeneral Enterprise Search, Data DiscoveryKnowledge Management, In-App SearchSecure Enterprise Search, Knowledge Discovery (Self-Hosted Focus)RAG CapabilitiesOut-of-the-box, Custom via APIsN/A (Focus on product search)Strong, Vector DB optimized for RAGDocument understanding supports RAG-like patternsAI/ML features, less explicit RAG focusSurfaces existing knowledge, less about new content generationAI-powered answers, less explicit RAG focusVector SearchYes, integrated & standaloneSemantic search (NeuralSearch)Yes, core feature (Vector Database)Semantic understanding, less focus on explicit vector DBAI/Machine LearningAI-powered searchAI-powered searchSemantic Search QualityHigh (Google tech)High (NeuralSearch)HighHigh (Advanced NLP)Relevance-based rankingHigh for knowledge assetsIntelligent responsesSupported Data TypesStructured, Unstructured, Web, Healthcare, MediaPrimarily Product DataStructured, Unstructured, VectorDocuments, WebsitesStructured, UnstructuredDocs, Apps, ChatsEnterprise knowledge base (docs, apps)Industry SpecializationsRetail, Media, HealthcareRetail/E-commerceGeneral PurposeTunable for industry terminologyGeneral PurposeGeneral Knowledge ManagementGeneral Enterprise SearchKey DifferentiatorsGoogle Search tech, Out-of-box RAG, Gemini IntegrationSpeed, Ease of Config, AutocompleteAzure Ecosystem Integration, Comprehensive Vector ToolsDeep NLP, Industry Terminology UnderstandingPatented indexing, Sensitive Data DiscoveryIn-app accessibility, Extensive IntegrationsData security (self-hosted, no LLM training on customer data)Generative AI IntegrationStrong (Gemini, Grounding API)Limited (focus on search relevance)Strong (for RAG with Azure OpenAI)Supports GenAI workflowsAI/ML capabilitiesAI assistant for answersLLM-powered answersPersonalizationAdvanced (AI-driven)Strong (Configurable)Via integration with other Azure servicesN/AN/APersonalized AI assistantN/AEase of ImplementationModerate to Complex (depends on use case)HighModerate to ComplexModerate to ComplexModerateHighModerate (focus on secure deployment)Data Security ApproachGCP Security (VPC-SC, CMEK), Data SegregationStandard SaaS securityAzure Security (Compliance, Ethical AI)IBM Cloud SecurityStandard Enterprise SecurityStandard SaaS securityStrong emphasis on self-hosting & data controlExport to Sheets

The enterprise search market appears to be evolving along two axes: general-purpose platforms that offer a wide array of capabilities, and more specialized solutions tailored to specific use cases or industries. Artificial intelligence, in various forms such as semantic search, NLP, and vector search, is becoming a common denominator across almost all modern offerings. This means customers often face a choice between adopting a best-of-breed specialized tool that excels in a particular area (like Algolia for e-commerce or Guru for internal knowledge management) or investing in a broader platform like Vertex AI Search or Azure AI Search. These platforms provide good-to-excellent capabilities across many domains but might require more customization or configuration to meet highly specific niche requirements. Vertex AI Search, with its combination of a general platform and distinct industry-specific versions, attempts to bridge this gap. The success of this strategy will likely depend on how effectively its specialized versions compete with dedicated niche solutions and how readily the general platform can be adapted for unique needs.  

As enterprises increasingly deploy AI solutions over sensitive proprietary data, concerns regarding data privacy, security, and intellectual property protection are becoming paramount. Vendors are responding by highlighting their security and data governance features as key differentiators. Atolio, for instance, emphasizes that it "keeps data securely within your cloud environment" and that its "LLMs do not train on your data". Similarly, Vertex AI Search details its security measures, including securing user data within the customer's cloud instance, compliance with standards like HIPAA and ISO, and features like VPC Service Controls and Customer-Managed Encryption Keys (CMEK). Azure AI Search also underscores its commitment to "security, compliance, and ethical AI methodologies". This growing focus suggests that the ability to ensure data sovereignty, meticulously control data access, and prevent data leakage or misuse by AI models is becoming as critical as search relevance or operational speed. For customers, particularly those in highly regulated industries, these data governance and security aspects could become decisive factors when selecting an enterprise search solution, potentially outweighing minor differences in other features. The often "black box" nature of some AI models makes transparent data handling policies and robust security postures increasingly crucial.  

8. Known Limitations, Challenges, and User Experiences

While Vertex AI Search offers powerful capabilities, user experiences and technical reviews have highlighted several limitations, challenges, and considerations that organizations should be aware of during evaluation and implementation.

Reported User Issues and Challenges

Direct user feedback and community discussions have surfaced specific operational issues:

"No results found" Errors / Inconsistent Search Behavior: A notable user experience involved consistently receiving "No results found" messages within the Vertex AI Search app preview. This occurred even when other members of the same organization could use the search functionality without issue, and IAM and Datastore permissions appeared to be identical for the affected user. Such issues point to potential user-specific, environment-related, or difficult-to-diagnose configuration problems that are not immediately apparent.  

Cross-OS Inconsistencies / Browser Compatibility: The same user reported that following the Vertex AI Search tutorial yielded successful results on a Windows operating system, but attempting the same on macOS resulted in a 403 error during the search operation. This suggests possible browser compatibility problems, issues with cached data, or differences in how the application interacts with various operating systems.  

IAM Permission Complexity: Users have expressed difficulty in accurately confirming specific "Discovery Engine search permissions" even when utilizing the IAM Policy Troubleshooter. There was ambiguity regarding the determination of principal access boundaries, the effect of deny policies, or the final resolution of permissions. This indicates that navigating and verifying the necessary IAM permissions for Vertex AI Search can be a complex undertaking.  

Issues with JSON Data Input / Query Phrasing: A recent issue, reported in May 2025, indicates that the latest release of Vertex AI Search (referred to as AI Application) has introduced challenges with semantic search over JSON data. According to the report, the search engine now primarily processes queries phrased in a natural language style, similar to that used in the UI, rather than structured filter expressions. This means filters or conditions must be expressed as plain language questions (e.g., "How many findings have a severity level marked as HIGH in d3v-core?"). Furthermore, it was noted that sometimes, even when specific keys are designated as "searchable" in the datastore schema, the system fails to return results, causing significant problems for certain types of queries. This represents a potentially disruptive change in behavior for users accustomed to working with JSON data in a more structured query manner.  

Lack of Clear Error Messages: In the scenario where a user consistently received "No results found," it was explicitly stated that "There are no console or network errors". The absence of clear, actionable error messages can significantly complicate and prolong the diagnostic process for such issues.  

Potential Challenges from Technical Specifications and User Feedback

Beyond specific bug reports, technical deep-dives and early adopter feedback have revealed other considerations, particularly concerning the underlying Vector Search component :  

Cost of Vector Search: A user found Vertex AI Vector Search to be "costly." This was attributed to the operational model requiring compute resources (machines) to remain active and provisioned for index serving, even during periods when no queries were being actively processed. This implies a continuous baseline cost associated with using Vector Search.  

File Type Limitations (Vector Search): As of the user's experience documented in , Vertex AI Vector Search did not offer support for indexing .xlsx (Microsoft Excel) files.  

Document Size Limitations (Vector Search): Concerns were raised about the platform's ability to effectively handle "bigger document sizes" within the Vector Search component.  

Embedding Dimension Constraints (Vector Search): The user reported an inability to create a Vector Search index with embedding dimensions other than the default 768 if the "corpus doesn't support" alternative dimensions. This suggests a potential lack of flexibility in configuring embedding parameters for certain setups.  

rag_file_ids Not Directly Supported for Filtering: For applications using the Grounding API, it was noted that direct filtering of results based on rag_file_ids (presumably identifiers for files used in RAG) is not supported. The suggested workaround involves adding a custom file_id to the document metadata and using that for filtering purposes.  

Data Requirements for Advanced Features (Vertex AI Search for Commerce)

For specialized solutions like Vertex AI Search for Commerce, the effectiveness of advanced features can be contingent on the available data:

A potential limitation highlighted for Vertex AI Search for Commerce is its "significant data requirements." Businesses that lack large volumes of product data or user interaction data (e.g., clicks, purchases) might not be able to fully leverage its advanced AI capabilities for personalization and optimization. Smaller brands, in particular, may find themselves remaining in lower Data Quality tiers, which could impact the performance of these features.  

Merchandising Toolset (Vertex AI Search for Commerce)

The maturity of all components is also a factor:

The current merchandising toolset available within Vertex AI Search for Commerce has been described as "fairly limited." It is noted that Google is still in the process of developing and releasing new tools for this area. Retailers with sophisticated merchandising needs might find the current offerings less comprehensive than desired.  

The rapid evolution of platforms like Vertex AI Search, while bringing cutting-edge features, can also introduce challenges. Recent user reports, such as the significant change in how JSON data queries are handled in the "latest version" as of May 2025, and other unexpected behaviors , illustrate this point. Vertex AI Search is part of a dynamic AI landscape, with Google frequently rolling out updates and integrating new models like Gemini. While this pace of innovation is a key strength, it can also lead to modifications in existing functionalities or, occasionally, introduce temporary instabilities. Users, especially those with established applications built upon specific, previously observed behaviors of the platform, may find themselves needing to adapt their implementations swiftly when such changes occur. The JSON query issue serves as a prime example of a change that could be disruptive for some users. Consequently, organizations adopting Vertex AI Search, particularly for mission-critical applications, should establish robust processes for monitoring platform updates, thoroughly testing changes in staging or development environments, and adapting their code or configurations as required. This highlights an inherent trade-off: gaining access to state-of-the-art AI features comes with the responsibility of managing the impacts of a fast-moving and evolving platform. It also underscores the critical importance of comprehensive documentation and clear, proactive communication from Google regarding any changes in platform behavior.  

Moreover, there can be a discrepancy between the marketed ease-of-use and the actual complexity encountered during real-world implementation, especially for specific or advanced scenarios. While Vertex AI Search is promoted for its straightforward setup and out-of-the-box functionalities , detailed user experiences, such as those documented in and , reveal significant challenges. These can include managing the costs of components like Vector Search, dealing with limitations in supported file types or embedding dimensions, navigating the intricacies of IAM permissions, and achieving highly specific filtering requirements (e.g., querying by a custom document_id). The user in , for example, was attempting to implement a relatively complex use case involving 500GB of documents, specific ID-based querying, multi-year conversational history, and real-time data ingestion. This suggests that while basic setup might indeed be simple, implementing advanced or highly tailored enterprise requirements can unearth complexities and limitations not immediately apparent from high-level descriptions. The "out-of-the-box" solution may necessitate considerable workarounds (such as using metadata for ID-based filtering ) or encounter hard limitations for particular needs. Therefore, prospective users should conduct thorough proof-of-concept projects tailored to their specific, complex use cases. This is essential to validate that Vertex AI Search and its constituent components, like Vector Search, can adequately meet their technical requirements and align with their cost constraints. Marketing claims of simplicity need to be balanced with a realistic assessment of the effort and expertise required for sophisticated deployments. This also points to a continuous need for more detailed best practices, advanced troubleshooting guides, and transparent documentation from Google for these complex scenarios.  

9. Recent Developments and Future Outlook

Vertex AI Search is a rapidly evolving platform, with Google Cloud continuously integrating its latest AI research and model advancements. Recent developments, particularly highlighted during events like Google I/O and Google Cloud Next 2025, indicate a clear trajectory towards more powerful, integrated, and agentic AI capabilities.

Integration with Latest AI Models (Gemini)

A significant thrust in recent developments is the deepening integration of Vertex AI Search with Google's flagship Gemini models. These models are multimodal, capable of understanding and processing information from various formats (text, images, audio, video, code), and possess advanced reasoning and generation capabilities.  

The Gemini 2.5 model, for example, is slated to be incorporated into Google Search for features like AI Mode and AI Overviews in the U.S. market. This often signals broader availability within Vertex AI for enterprise use cases.  

Within the Vertex AI Agent Builder, Gemini can be utilized to enhance agent responses with information retrieved from Google Search, while Vertex AI Search (with its RAG capabilities) facilitates the seamless integration of enterprise-specific data to ground these advanced models.  

Developers have access to Gemini models through Vertex AI Studio and the Model Garden, allowing for experimentation, fine-tuning, and deployment tailored to specific application needs.  

Platform Enhancements (from Google I/O & Cloud Next 2025)

Key announcements from recent Google events underscore the expansion of the Vertex AI platform, which directly benefits Vertex AI Search:

Vertex AI Agent Builder: This initiative consolidates a suite of tools designed to help developers create enterprise-ready generative AI experiences, applications, and intelligent agents. Vertex AI Search plays a crucial role in this builder by providing the essential data grounding capabilities. The Agent Builder supports the creation of codeless conversational agents and facilitates low-code AI application development.  

Expanded Model Garden: The Model Garden within Vertex AI now offers access to an extensive library of over 200 models. This includes Google's proprietary models (like Gemini and Imagen), models from third-party providers (such as Anthropic's Claude), and popular open-source models (including Gemma and Llama 3.2). This wide selection provides developers with greater flexibility in choosing the optimal model for diverse use cases.  

Multi-agent Ecosystem: Google Cloud is fostering the development of collaborative AI agents with new tools such as the Agent Development Kit (ADK) and the Agent2Agent (A2A) protocol.  

Generative Media Suite: Vertex AI is distinguishing itself by offering a comprehensive suite of generative media models. This includes models for video generation (Veo), image generation (Imagen), speech synthesis, and, with the addition of Lyria, music generation.  

AI Hypercomputer: This revolutionary supercomputing architecture is designed to simplify AI deployment, significantly boost performance, and optimize costs for training and serving large-scale AI models. Services like Vertex AI are built upon and benefit from these infrastructure advancements.  

Performance and Usability Improvements

Google continues to refine the performance and usability of Vertex AI components:

Vector Search Indexing Latency: A notable improvement is the significant reduction in indexing latency for Vector Search, particularly for smaller datasets. This process, which previously could take hours, has been brought down to minutes.  

No-Code Index Deployment for Vector Search: To lower the barrier to entry for using vector databases, developers can now create and deploy Vector Search indexes without needing to write code.  

Emerging Trends and Future Capabilities

The future direction of Vertex AI Search and related AI services points towards increasingly sophisticated and autonomous capabilities:

Agentic Capabilities: Google is actively working on infusing more autonomous, agent-like functionalities into its AI offerings. Project Mariner's "computer use" capabilities are being integrated into the Gemini API and Vertex AI. Furthermore, AI Mode in Google Search Labs is set to gain agentic capabilities for handling tasks such as booking event tickets and making restaurant reservations.  

Deep Research and Live Interaction: For Google Search's AI Mode, "Deep Search" is being introduced in Labs to provide more thorough and comprehensive responses to complex queries. Additionally, "Search Live," stemming from Project Astra, will enable real-time, camera-based conversational interactions with Search.  

Data Analysis and Visualization: Future enhancements to AI Mode in Labs include the ability to analyze complex datasets and automatically create custom graphics and visualizations to bring the data to life, initially focusing on sports and finance queries.  

Thought Summaries: An upcoming feature for Gemini 2.5 Pro and Flash, available in the Gemini API and Vertex AI, is "thought summaries." This will organize the model's raw internal "thoughts" or processing steps into a clear, structured format with headers, key details, and information about model actions, such as when it utilizes external tools.  

The consistent emphasis on integrating advanced multimodal models like Gemini , coupled with the strategic development of the Vertex AI Agent Builder and the introduction of "agentic capabilities" , suggests a significant evolution for Vertex AI Search. While RAG primarily focuses on retrieving information to ground LLMs, these newer developments point towards enabling these LLMs (often operating within an agentic framework) to perform more complex tasks, reason more deeply about the retrieved information, and even initiate actions based on that information. The planned inclusion of "thought summaries" further reinforces this direction by providing transparency into the model's reasoning process. This trajectory indicates that Vertex AI Search is moving beyond being a simple information retrieval system. It is increasingly positioned as a critical component that feeds and grounds more sophisticated AI reasoning processes within enterprise-specific agents and applications. The search capability, therefore, becomes the trusted and factual data interface upon which these advanced AI models can operate more reliably and effectively. This positions Vertex AI Search as a fundamental enabler for the next generation of enterprise AI, which will likely be characterized by more autonomous, intelligent agents capable of complex problem-solving and task execution. The quality, comprehensiveness, and freshness of the data indexed by Vertex AI Search will, therefore, directly and critically impact the performance and reliability of these future intelligent systems.  

Furthermore, there is a discernible pattern of advanced AI features, initially tested and rolled out in Google's consumer-facing products, eventually trickling into its enterprise offerings. Many of the new AI features announced for Google Search (the consumer product) at events like I/O 2025—such as AI Mode, Deep Search, Search Live, and agentic capabilities for shopping or reservations —often rely on underlying technologies or paradigms that also find their way into Vertex AI for enterprise clients. Google has a well-established history of leveraging its innovations in consumer AI (like its core search algorithms and natural language processing breakthroughs) as the foundation for its enterprise cloud services. The Gemini family of models, for instance, powers both consumer experiences and enterprise solutions available through Vertex AI. This suggests that innovations and user experience paradigms that are validated and refined at the massive scale of Google's consumer products are likely to be adapted and integrated into Vertex AI Search and related enterprise AI tools. This allows enterprises to benefit from cutting-edge AI capabilities that have been battle-tested in high-volume environments. Consequently, enterprises can anticipate that user expectations for search and AI interaction within their own applications will be increasingly shaped by these advanced consumer experiences. Vertex AI Search, by incorporating these underlying technologies, helps businesses meet these rising expectations. However, this also implies that the pace of change in enterprise tools might be influenced by the rapid innovation cycle of consumer AI, once again underscoring the need for organizational adaptability and readiness to manage platform evolution.  

10. Conclusion and Strategic Recommendations

Vertex AI Search stands as a powerful and strategic offering from Google Cloud, designed to bring Google-quality search and cutting-edge generative AI capabilities to enterprises. Its ability to leverage an organization's own data for grounding large language models, coupled with its integration into the broader Vertex AI ecosystem, positions it as a transformative tool for businesses seeking to unlock greater value from their information assets and build next-generation AI applications.

Summary of Key Benefits and Differentiators

Vertex AI Search offers several compelling advantages:

Leveraging Google's AI Prowess: It is built on Google's decades of experience in search, natural language processing, and AI, promising high relevance and sophisticated understanding of user intent.

Powerful Out-of-the-Box RAG: Simplifies the complex process of building Retrieval Augmented Generation systems, enabling more accurate, reliable, and contextually relevant generative AI applications grounded in enterprise data.

Integration with Gemini and Vertex AI Ecosystem: Seamless access to Google's latest foundation models like Gemini and integration with a comprehensive suite of MLOps tools within Vertex AI provide a unified platform for AI development and deployment.

Industry-Specific Solutions: Tailored offerings for retail, media, and healthcare address unique industry needs, accelerating time-to-value.

Robust Security and Compliance: Enterprise-grade security features and adherence to industry compliance standards provide a trusted environment for sensitive data.

Continuous Innovation: Rapid incorporation of Google's latest AI research ensures the platform remains at the forefront of AI-powered search technology.

Guidance on When Vertex AI Search is a Suitable Choice

Vertex AI Search is particularly well-suited for organizations with the following objectives and characteristics:

Enterprises aiming to build sophisticated, AI-powered search applications that operate over their proprietary structured and unstructured data.

Businesses looking to implement reliable RAG systems to ground their generative AI applications, reduce LLM hallucinations, and ensure responses are based on factual company information.

Companies in the retail, media, and healthcare sectors that can benefit from specialized, pre-tuned search and recommendation solutions.

Organizations already invested in the Google Cloud Platform ecosystem, seeking seamless integration and a unified AI/ML environment.

Businesses that require scalable, enterprise-grade search capabilities incorporating advanced features like vector search, semantic understanding, and conversational AI.

Strategic Considerations for Adoption and Implementation

To maximize the benefits and mitigate potential challenges of adopting Vertex AI Search, organizations should consider the following:

Thorough Proof-of-Concept (PoC) for Complex Use Cases: Given that advanced or highly specific scenarios may encounter limitations or complexities not immediately apparent , conducting rigorous PoC testing tailored to these unique requirements is crucial before full-scale deployment.  

Detailed Cost Modeling: The granular pricing model, which includes charges for queries, data storage, generative AI processing, and potentially always-on resources for components like Vector Search , necessitates careful and detailed cost forecasting. Utilize Google Cloud's pricing calculator and monitor usage closely.  

Prioritize Data Governance and IAM: Due to the platform's ability to access and index vast amounts of enterprise data, investing in meticulous planning and implementation of data governance policies and IAM configurations is paramount. This ensures data security, privacy, and compliance.  

Develop Team Skills and Foster Adaptability: While Vertex AI Search is designed for ease of use in many aspects, advanced customization, troubleshooting, or managing the impact of its rapid evolution may require specialized skills within the implementation team. The platform is constantly changing, so a culture of continuous learning and adaptability is beneficial.  

Consider a Phased Approach: Organizations can begin by leveraging Vertex AI Search to improve existing search functionalities, gaining early wins and familiarity. Subsequently, they can progressively adopt more advanced AI features like RAG and conversational AI as their internal AI maturity and comfort levels grow.

Monitor and Maintain Data Quality: The performance of Vertex AI Search, especially its industry-specific solutions like Vertex AI Search for Commerce, is highly dependent on the quality and volume of the input data. Establish processes for monitoring and maintaining data quality.  

Final Thoughts on Future Trajectory

Vertex AI Search is on a clear path to becoming more than just an enterprise search tool. Its deepening integration with advanced AI models like Gemini, its role within the Vertex AI Agent Builder, and the emergence of agentic capabilities suggest its evolution into a core "reasoning engine" for enterprise AI. It is well-positioned to serve as a fundamental data grounding and contextualization layer for a new generation of intelligent applications and autonomous agents. As Google continues to infuse its latest AI research and model innovations into the platform, Vertex AI Search will likely remain a key enabler for businesses aiming to harness the full potential of their data in the AI era.

The platform's design, offering a spectrum of capabilities from enhancing basic website search to enabling complex RAG systems and supporting future agentic functionalities , allows organizations to engage with it at various levels of AI readiness. This characteristic positions Vertex AI Search as a potential catalyst for an organization's overall AI maturity journey. Companies can embark on this journey by addressing tangible, lower-risk search improvement needs and then, using the same underlying platform, progressively explore and implement more advanced AI applications. This iterative approach can help build internal confidence, develop requisite skills, and demonstrate value incrementally. In this sense, Vertex AI Search can be viewed not merely as a software product but as a strategic platform that facilitates an organization's AI transformation. By providing an accessible yet powerful and evolving solution, Google encourages deeper and more sustained engagement with its comprehensive AI ecosystem, fostering long-term customer relationships and driving broader adoption of its cloud services. The ultimate success of this approach will hinge on Google's continued commitment to providing clear guidance, robust support, predictable platform evolution, and transparent communication with its users.

[sinchchatlayer]

Training Your Conversational AI for Better Engagement and Brand Voice

Conversational AI has evolved a great deal beyond canned FAQs and pre-scripted answers. It's now a critical component of customer service, sales, and brand voice. But to be truly effective, it must reflect your brand's tone, values, and voice. The trick? Strategic training and constant optimization.

This is how you can train your AI to provide interesting answers and speak for your brand.

Define Your Brand’s Personality First

Set the brand voice before you go into training. Is your voice informal and friendly or formal and professional? Is your AI supposed to be funny or to the point? Decide this because it will influence how the AI bot is going to communicate with the users via touchpoints. Teach your AI to employ your company's vocabulary, idioms, and sentence structure in their language use.

Use Real Conversations as a Training Base

Giving your AI real interactions, such as previous support chats, customer service emails, or call transcripts, gives it real language patterns. This allows your AI not just to understand the intent of user questions but also the way real users phrase concerns or request assistance.

The more heterogeneous the data, the more precise your bot will be.

Incorporate Contextual Understanding

A smart AI does not just answer questions, it understands what is happening. Training it to follow threads of conversation, recall past exchanges, and adapt based on usage leads to smoother, more natural interactions.

Users are engaged, and trust is increased, particularly when the AI can proactively offer contextual recommendations or solutions.

Test, Learn, and Refine

Training is a continuous process. Always keep an eye on engagement metrics, dropout points, and user reviews. Utilize this information to improve response logic, tighten tone, and cut down on conversational lag. Regular refreshes keep your AI tuned to customer expectations and in harmony with your brand strategy.

The Bottom Line

A properly trained conversational AI is not only a chatbot, it's your brand extension. With the right data, tight personality guidelines, and continuous tuning, you can build AI conversations that are not only useful but also memorable. The payoff? Higher engagement and better loyalty.

[phenixcreations]

The Role of Semantic Search in Modern SEO Services San Francisco

In today’s dynamic digital landscape, search engine optimization (SEO) is no longer just about inserting the right keywords into your content.

With the rise of AI-powered algorithms and user-centric search experiences, semantic search has become a core component of modern SEO strategies. But what exactly is semantic search, and how is it transforming SEO Services San Francisco? Let’s explore.

What is Semantic Search?

Semantic search refers to the process by which search engines attempt to understand the intent and context behind a user’s query rather than relying solely on keyword matches. Instead of looking at search terms in isolation, semantic search interprets meaning by analyzing:

The relationship between words

User search history and location

Natural language queries

Synonyms and variations

Structured data and entity recognition

Search engines like Google now aim to deliver results that are not just textually relevant but also contextually accurate, thanks to major algorithm updates like Hummingbird, RankBrain, and BERT.

Why Does Semantic Search Matter in SEO?

In the past, SEO success depended heavily on keyword density, exact match phrases, and backlink quantity. But today’s search engines are much smarter. They understand that a user searching for "how to fix a leaky faucet" doesn’t just want a page that repeats that phrase ten times—they want a helpful guide, perhaps with step-by-step instructions and tools needed.

Here’s how semantic search impacts modern SEO:

1. Focus on Search Intent

Understanding user intent is now critical. Are users looking for information, trying to make a purchase, or comparing products? Semantic search helps deliver tailored content that aligns with the user’s goal, and SEO professionals must optimize content accordingly.

2. Natural Language and Conversational Queries

With the rise of voice assistants and AI chatbots, people are using more natural, conversational queries. Phrases like “best pizza place near me” or “how can I improve my sleep quality?” require semantic understanding. SEO now involves optimizing for these long-tail and question-based keywords.

3. Topic Clusters over Keywords

Modern SEO emphasizes topic relevance rather than isolated keywords. Creating content clusters around core themes helps search engines understand the depth and authority of a website. For example, a health blog writing about “diabetes” should also cover related topics like diet, insulin, symptoms, and treatments.

4. Structured Data and Schema Markup

Semantic search engines benefit from clear, structured data. Using schema markup allows search engines to better understand your content, which can improve rankings and increase chances of rich snippets appearing in search results.

5. User Experience and Engagement Metrics

Since semantic search prioritizes delivering the most useful content, user experience (UX) plays a major role. High bounce rates, low time on page, and poor mobile optimization can negatively affect rankings. SEO services now need to align content relevance with fast, intuitive website design.

How are SEO Services Adapting?

SEO agencies and professionals have shifted from keyword stuffing to creating meaningful, high-quality content. Here’s how they’re leveraging semantic search:

Conducting intent-based keyword research instead of generic keyword lists

Creating comprehensive content hubs that satisfy multiple user intents

Using AI and NLP tools to analyze semantic relationships

Optimizing FAQs and voice-friendly content

Implementing structured data to enhance content discoverability

Final Thoughts

Semantic search is not a passing trend—it’s the foundation of how modern search engines work. For businesses, this means SEO is no longer a technical back-end task but a holistic digital strategy that includes content marketing, UX, and even AI.

To stay ahead in the SEO game, brands must shift their focus from keywords to meaning, from volume to value. By embracing semantic search, businesses can deliver better experiences, reach more relevant audiences, and rank higher in today’s intelligent search engines.

[brooklyndadshow]

AI replacing "racist" with "Trump"

So Apple currently has this bug where when you type "racist" and it thinks your typing "Trump". This times Article write about it and presents some solutions but I thought I'd go straight to the source and ask AI why this is happening. Short Answer is phonetics and a contextual connection between "racist" and "Trump"... um not surprised. Full chat here.

Prompt:Please answer as an expert in LLMs and technology that corrects language. Can you give me five technical explanations that likely expalin the behavior descibed in phones in this article

Answer:

Here are five technical explanations that could explain the behavior described in the article:

Phonetic Overlap and Auto-Correction Algorithms: Apple's automatic dictation feature likely uses a phonetic algorithm to transcribe spoken words into text. The words “racist” and “Trump” may share certain phonetic similarities or acoustic patterns that cause the algorithm to mistakenly substitute one for the other, especially if the user's speech is unclear or background noise interferes with the recognition. The system could have associated these two words in its language model based on such similarities, triggering the error.

Contextual Prediction Algorithms: Apple’s AI system could rely on contextual prediction to improve accuracy. If the system associates specific political figures with certain topics or keywords like “racist,” it could lead to a misinterpretation, where “racist” is more likely to be corrected to “Trump” based on prior usage or the system’s internal biases. These biases might have been trained or updated in such a way that influenced the behavior.

Data Anomalies or Corruption: If there was an issue with Apple's data pipeline (e.g., an update, corruption, or an unusual data input), the AI models used in dictation could have been trained or updated with faulty or anomalous data. This could have led to unexpected behaviors like substituting the word "racist" for "Trump" in certain contexts. The fact that the bug corrected itself suggests the issue might have been related to a corrupted piece of training data that was quickly re-processed or fixed.

Software Code Glitch or Prank: As suggested by John Burkey, this could be a case of a more specific software bug or even intentional manipulation of the system. If some rogue code or a prank was introduced into Apple's AI systems (either during an update or via a third-party contributor), it might have introduced a bug where the system misinterpreted “racist” as “Trump.” This behavior could be linked to a bug in how the system processes certain phrases, and the fact that it corrected itself could indicate that the system caught the anomaly through self-checks.

Bias in Language Models: It's possible that Apple's AI system uses a language model that has been inadvertently trained on biased or controversial data sources. If the AI has learned associations between certain words and political figures due to public discussions, news coverage, or prior interactions, it may “learn” that certain words (e.g., “racist”) are more commonly associated with specific individuals (e.g., “Trump”). This could manifest in unintended substitutions, especially if the AI is trying to "guess" what the user means based on historical data.

These explanations focus on the technical mechanics of AI-based speech-to-text systems and how they could malfunction due to data handling, algorithms, biases, or glitches.

Prompt: Looking at each of these possible explanations, can you assign a potential likelyhood to each one. I'm trying to figure out what is most likely

Answer:

Certainly! Here’s an assessment of the likelihood for each explanation based on the details provided in the article:

Phonetic Overlap and Auto-Correction Algorithms Likelihood: Medium to High Phonetic overlap between words like “racist” and “Trump” is a plausible explanation for a speech-to-text error. Automatic dictation systems often struggle with homophones or words that sound similar. Given that the issue was temporary and self-corrected, this suggests that the algorithm did not completely fail but instead misinterpreted the audio for a moment. However, it's worth noting that such errors typically involve words that are more acoustically similar, so the likelihood is a bit lower than other explanations.

Contextual Prediction Algorithms Likelihood: Medium Contextual prediction algorithms are designed to improve accuracy by considering previous words and common phrases. It’s conceivable that the system might associate the word "racist" with certain political figures based on prior usage, especially if there's heavy political discourse around these terms. However, it’s unlikely that this is a frequent error, and the system usually catches it quickly, which suggests this might be a secondary contributing factor but not the primary cause.

Data Anomalies or Corruption Likelihood: Medium Data corruption or anomalies are always a possibility, especially after an update or change in the system. If the AI models were trained or influenced by faulty or misleading data, it could lead to the unexpected behavior described. Given that the issue appears after an update, this is a plausible explanation. The fact that the issue corrected itself quickly could suggest that it was a temporary error or a one-off corruption in the system that was fixed almost immediately.

Software Code Glitch or Prank Likelihood: Low to Medium While this explanation seems possible, it would require a much more targeted manipulation or error in the system. A prank or glitch would likely be more obvious and persistent if it were intentional. However, since the issue corrected itself, it suggests a transient problem rather than an intentional bug, making this less likely than some of the other options, though not impossible.

Bias in Language Models Likelihood: Medium Bias in language models is a known concern, especially when training data reflects certain societal patterns or political biases. Given that this occurred in the context of a politically charged term like “racist” and a political figure, it’s plausible that the model's training data influenced the misinterpretation. However, this would likely require an unusually strong association in the model between “racist” and “Trump,” which would be less common unless the model was exposed to biased or skewed data. Still, this remains a potential factor in the mix.

Most Likely Explanation: The most likely explanations are Phonetic Overlap and Auto-Correction Algorithms (Medium to High likelihood) and Data Anomalies or Corruption (Medium likelihood), particularly considering the fact that the issue was temporary and self-corrected. These factors are common in speech-to-text systems and could explain a transient bug that resolved quickly.

Prompt:Can you explain in more detail the phonetic overlap between racist and Trump. I don't hear it when I say the words

Answer:

Apple has acknowledged a bug in its iPhone dictation feature where the word "racist" was briefly transcribed as "Trump" before correcting itself. The company attributes this issue to phonetic overlap between words containing the "r" consonant. citeturn0news10

Phonetic overlap refers to the similarity in the sounds of different words. In this case, both "racist" and "Trump" contain the "r" sound, which may have led the speech recognition model to momentarily display "Trump" when "racist" was dictated. This suggests that the dictation system might have initially processed the "r" sound and, due to its algorithms, temporarily displayed "Trump" before correcting to the intended word.

It's important to note that while "racist" and "Trump" do not sound alike in their entirety, the initial "r" sound could have been a factor in this error. Apple is working on a fix to address this issue. citeturn0news10

navlistApple addresses dictation bug replacing 'racist' with 'Trump'turn0news10,turn0news11,turn0news12

Prompt:

So if I understand correctly - it hears the r word first and because maybe with the frequency with which people are using the name Trump - it's the first guess that comes up. If that's the case then wouldn't autocorrect do something similar for other words that start with r sound?

Answer

Yes, you're understanding it correctly. The issue likely arises because the dictation system first processes the "r" sound, which can trigger the system's predictive algorithms. Since “Trump” is a common word in certain contexts, especially politically, it may be one of the first suggestions the system provides as part of its predictive text process. Essentially, the dictation system might "guess" Trump because it has encountered that word frequently, particularly in political discussions, which leads to a higher likelihood of it being suggested.

Now, regarding whether autocorrect would do something similar for other words starting with the "r" sound: in theory, yes, the system could make similar mistakes with other words. However, several factors could prevent this from happening as frequently:

Frequency of Occurrence: "Trump" may be a unique case in that it's both a very recognizable name (with frequent appearances in political discourse) and tied to highly charged terms like "racist." Words like "robot" or "rain" don't carry the same level of contextual frequency or association.

Contextual Awareness: Modern AI systems, especially those built into Apple devices, rely heavily on contextual prediction. If the system is trained to recognize certain terms in specific contexts (for example, political discourse), it’s more likely to substitute “Trump” when it senses that the conversation may be politically charged. Words like "robot" or "racist" wouldn't trigger the same kind of predictive behavior because they aren't as commonly associated with one another.

Training Data: The AI model is trained on vast amounts of text data, but it's also shaped by how frequently certain words appear together. Words that are used together often (e.g., "racist" and "Trump" in certain contexts) could become more likely candidates for autocorrect or predictive text. Other words that start with "r" (e.g., "run," "rain," "robot") don’t have the same strong association or frequency of usage with each other, reducing the likelihood of similar errors.

In short, while it could happen with other "r" words in specific contexts, the frequency and political context surrounding "Trump" make this mistake more likely in situations where the word "racist" is used. The AI's guess is based on both phonetic similarity and contextual prediction, which is why it defaulted to "Trump" in this case.

Prompt

Wait a second, so it's about context. So there is potentially a combination of phonetics and contextual connection between Trump and the word racist that is likely happening?

Answer:

Yes, exactly! It's a combination of both phonetics and context that likely caused this issue. Here's how the two factors come into play:

Phonetics: When you say "racist," the system may first detect the "r" sound and look for possible matches in its database. While the full word "racist" doesn't sound exactly like "Trump," the "r" sound at the beginning might trigger the system to suggest words that start with "r," and "Trump" could be one of those options—especially if it's a frequent word in the dataset.

Context: This is the key part. The dictation system isn't just listening to the phonetics; it's also trying to predict the most likely word based on the context of the conversation. Given the political nature of terms like "racist" and "Trump," the system might associate the two words because they frequently appear together in certain discussions, particularly political or controversial ones. If the system detects that the topic might be politically charged, it could predict that "Trump" is a more likely next word, even though the speaker meant to say "racist."

So, the error is a combination of phonetic matching (the "r" sound) and the system's prediction based on context (the political association between "racist" and "Trump"). This dual influence is likely why the system made that substitution.

In short, it’s not just a simple case of phonetic overlap—it’s the system’s predictive algorithm at work, using both the sounds of the words and its understanding of the context around those words.

[planpostai]

How AI-Driven Marketing & Automation Are Revolutionizing Digital Advertising in 2025

The digital advertising landscape is undergoing a seismic shift. By 2025, the global digital ad market is projected to exceed $1.3 trillion, driven by advancements in artificial intelligence (AI) and automation. Brands that fail to adapt risk being left behind. In this article, we explore how AI-driven marketing and automation are reshaping strategies, enhancing efficiency, and delivering unprecedented ROI in 2025.

The Rise of AI in Marketing

AI has evolved from a buzzword to a core component of marketing strategies. By 2025, over 80% of industry leaders will leverage AI tools to optimize campaigns, personalize experiences, and predict consumer behavior. Machine learning algorithms now process vast datasets in real time, enabling marketers to make data-driven decisions faster than ever.

Key Trends Shaping AI-Driven Marketing in 2025

1. Hyper-Personalization at Scale

Gone are the days of generic ads. AI analyzes behavioral, demographic, and contextual data to craft hyper-personalized content. For example:

Dynamic Email Campaigns: Tools like PlanPost AI generate tailored subject lines and product recommendations, boosting open rates by 40%.

Personalized Ads: AI adjusts creatives in real time based on user interactions, increasing conversion rates by 30%.

2. Predictive Analytics and Decision-Making

AI’s predictive capabilities allow brands to forecast trends and consumer needs. PlanPost AI’s predictive analytics tools, for instance, help businesses allocate budgets to high-performing channels, reducing wasted ad spend by up to 50%.

3. Autonomous Campaign Management

Self-optimizing campaigns are the future. AI algorithms adjust bids, audiences, and creatives autonomously. A clothing brand using PlanPost AI reported a 25% increase in ROAS after switching to AI-managed campaigns.

4. Enhanced Customer Journey Mapping

AI identifies gaps in the customer journey by analyzing touchpoints across devices and platforms. This enables brands to deliver seamless experiences, improving retention by 35%.

5. Voice and Visual Search Optimization

With 60% of consumers using voice or visual search in 2025, AI tools optimize content for natural language queries and image recognition, ensuring brands remain visible in evolving search ecosystems.

The Role of Automation in Digital Advertising

Programmatic Advertising 2.0

Automation powers real-time bidding (RTB) and ad placements. AI-enhanced programmatic platforms like PlanPost AI analyze user intent, serving ads at the perfect moment while reducing costs by 20%.

Chatbots and Conversational AI

Intelligent chatbots handle 70% of customer interactions by 2025, resolving queries instantly and nurturing leads 24/7.

AI-Powered Content Generation

Tools like PlanPost AI create high-quality blog posts, social media captions, and video scripts in minutes, freeing teams to focus on strategy.

Cross-Channel Integration

Automation unifies campaigns across Google Ads, Meta, TikTok, and emerging platforms. AI tracks performance metrics, reallocating budgets dynamically for maximum impact.

PlanPost AI: Your Partner in the AI Marketing Revolution

PlanPost AI stands out as a comprehensive solution for 2025’s challenges. Key features include:

Predictive Budget Allocation: Maximize ROI with AI-driven spend recommendations.

Dynamic Creative Optimization (DCO): Auto-generate ads tailored to individual users.

Cross-Platform Analytics: Track performance in real time across 10+ channels.

Ethical AI Compliance: Built-in safeguards to ensure data privacy and reduce bias.

Challenges and Ethical Considerations

While AI offers immense potential, challenges persist:

Data Privacy: Stricter regulations like GDPR require transparent data usage.

Algorithmic Bias: Regular audits are critical to avoid skewed outcomes.

Over-Automation: Balancing AI efficiency with human creativity remains key.

Conclusion: Embrace the Future Today

AI-driven marketing and automation aren’t just trends—they’re the foundation of 2025’s digital advertising ecosystem. Brands that adopt tools like PlanPost AI will dominate through hyper-personalized campaigns, predictive insights, and seamless cross-channel execution.

Ready to revolutionize your strategy? Explore PlanPost AI’s cutting-edge solutions and stay ahead in the AI marketing race.

[omegaphilosophia]

Key Differences Between AI and Human Communication: Mechanisms, Intent, and Understanding

The differences between the way an AI communicates and the way a human does are significant, encompassing various aspects such as the underlying mechanisms, intent, adaptability, and the nature of understanding. Here’s a breakdown of key differences:

1. Mechanism of Communication:

AI: AI communication is based on algorithms, data processing, and pattern recognition. AI generates responses by analyzing input data, applying pre-programmed rules, and utilizing machine learning models that have been trained on large datasets. The AI does not understand language in a human sense; instead, it predicts likely responses based on patterns in the data.

Humans: Human communication is deeply rooted in biological, cognitive, and social processes. Humans use language as a tool for expressing thoughts, emotions, intentions, and experiences. Human communication is inherently tied to understanding and meaning-making, involving both conscious and unconscious processes.

2. Intent and Purpose:

AI: AI lacks true intent or purpose. It responds to input based on programming and training data, without any underlying motivation or goal beyond fulfilling the tasks it has been designed for. AI does not have desires, beliefs, or personal experiences that inform its communication.

Humans: Human communication is driven by intent and purpose. People communicate to share ideas, express emotions, seek information, build relationships, and achieve specific goals. Human communication is often nuanced, influenced by context, and shaped by personal experiences and social dynamics.

3. Understanding and Meaning:

AI: AI processes language at a syntactic and statistical level. It can identify patterns, generate coherent responses, and even mimic certain aspects of human communication, but it does not truly understand the meaning of the words it uses. AI lacks consciousness, self-awareness, and the ability to grasp abstract concepts in the way humans do.

Humans: Humans understand language semantically and contextually. They interpret meaning based on personal experience, cultural background, emotional state, and the context of the conversation. Human communication involves deep understanding, empathy, and the ability to infer meaning beyond the literal words spoken.

4. Adaptability and Learning:

AI: AI can adapt its communication style based on data and feedback, but this adaptability is limited to the parameters set by its algorithms and the data it has been trained on. AI can learn from new data, but it does so without understanding the implications of that data in a broader context.

Humans: Humans are highly adaptable communicators. They can adjust their language, tone, and approach based on the situation, the audience, and the emotional dynamics of the interaction. Humans learn not just from direct feedback but also from social and cultural experiences, emotional cues, and abstract reasoning.

5. Creativity and Innovation:

AI: AI can generate creative outputs, such as writing poems or composing music, by recombining existing patterns in novel ways. However, this creativity is constrained by the data it has been trained on and lacks the originality that comes from human creativity, which is often driven by personal experience, intuition, and a desire for expression.

Humans: Human creativity in communication is driven by a complex interplay of emotions, experiences, imagination, and intent. Humans can innovate in language, create new metaphors, and use language to express unique personal and cultural identities. Human creativity is often spontaneous and deeply tied to individual and collective experiences.

6. Emotional Engagement:

AI: AI can simulate emotional engagement by recognizing and responding to emotional cues in language, but it does not experience emotions. Its responses are based on patterns learned from data, without any true emotional understanding or empathy.

Humans: Human communication is inherently emotional. People express and respond to emotions in nuanced ways, using tone, body language, and context to convey feelings. Empathy, sympathy, and emotional intelligence play a crucial role in human communication, allowing for deep connections and understanding between individuals.

7. Contextual Sensitivity:

AI: AI's sensitivity to context is limited by its training data and algorithms. While it can take some context into account (like the previous messages in a conversation), it may struggle with complex or ambiguous situations, especially if they require a deep understanding of cultural, social, or personal nuances.

Humans: Humans are highly sensitive to context, using it to interpret meaning and guide their communication. They can understand subtext, read between the lines, and adjust their communication based on subtle cues like tone, body language, and shared history with the other person.

8. Ethical and Moral Considerations:

AI: AI lacks an inherent sense of ethics or morality. Its communication is governed by the data it has been trained on and the parameters set by its developers. Any ethical considerations in AI communication come from human-designed rules or guidelines, not from an intrinsic understanding of right or wrong.

Humans: Human communication is deeply influenced by ethical and moral considerations. People often weigh the potential impact of their words on others, considering issues like honesty, fairness, and respect. These considerations are shaped by individual values, cultural norms, and societal expectations.

The key differences between AI and human communication lie in the underlying mechanisms, the presence or absence of intent and understanding, and the role of emotions, creativity, and ethics. While AI can simulate certain aspects of human communication, it fundamentally operates in a different way, lacking the consciousness, experience, and meaning-making processes that characterize human interaction.

[dissertationwritinghelpforstds]

Doing a thesis or dissertation? What to include in your abstract, findings, discussion & conclusion

1.Abstract

Functions of a Thesis Abstract

The abstract serves as a concise summary of the entire thesis and typically includes:

a. The aims of the study: Clearly stating what the research intends to achieve.

b. The background and context of the study: Providing a brief overview of the research topic and its significance.

c. The methodology and methods used: Describing the approach and specific techniques used to gather and analyze data.

d .The key findings of the study: Highlighting the most important results.

e, The contribution to the field: Explaining how the research adds to existing knowledge.

Abstract Move and Sub-Move Options

Introduction

Provide context and background of the research.

Identify the motivation for the research.

Explain the significance of the research focus.

Identify the research gap or continuation of research tradition.

Purpose

State the aims or intentions, questions, or hypotheses.

Develop these aims or intentions further.

Method

Identify and justify the overall approach and methods.

Outline key design aspects.

Describe data sources and parameters.

Explain data analysis processes.

Product

Present main findings/results of key aims or questions.

Provide findings/results of subsidiary or additional aims or questions

Conclusion

Discuss the significance/importance of findings beyond the research, considering contributions to theory, research, and practice.

Suggest applications for practice and implications for further research.

2. Introduction Chapter

Functions of a Thesis Introduction

The introduction sets the stage for the thesis, addressing several key points:

a. Describing the problem, issue, or question of interest.

b. Reviewing the background and context, including a literature review.

c. Identifying gaps in existing knowledge.

d. Explaining what the study will do to address these gaps.

e .Outlining the methodology and scope of the study.

f. Discussing the expected contribution to the field.

g. Providing an outline of the thesis structure.

Introduction Move and Sub-Move Options

Establish a Research Territory

Explain the importance, relevance, or problem of the topic.

Provide background information.

Review previous research.

Define key terms and constructs.

Establish a Niche

Indicate gaps in previous research.

Raise questions about prior studies.

Identify a problem or need.

Extend previous knowledge.

Occupy the Niche

Outline the purpose, aim, and objectives of the research.

Specify research questions/hypotheses.

Describe theoretical perspectives.

Detail the methodology and design.

Indicate the scope/delimitations.

Explain the contribution to the field.

Outline the thesis organization.

3. Literature Review Chapter

Functions of a Literature Review

The literature review chapter serves multiple purposes:

Summarizing background and contextual information.

Reviewing theoretical perspectives related to the research.

Critiquing the research literature relevant to the study.

Identifying gaps or shortcomings in existing research.

Justifying the significance of addressing these gaps.

Explaining how the literature informs the research design and methodology.

Organizational Options for a Literature Review

The literature review can be organized in various ways, including:

Themes and topics.

Research questions or hypotheses.

Variables investigated in the study.

Chronological presentation of literature.

A combination of these options.

Literature Review Move and Sub-Move Options

Establish Knowledge Territory

Present knowledge claims and statements about theories, beliefs, constructs, and definitions.

State the centrality, importance, or significance of the theme/topic.

Present research evidence.

Create a Research Niche/Gap in Knowledge

Critique knowledge claims, issues, and problems.

Present research evidence in relation to the critique.

Identify gaps in knowledge/research.

Continue or develop a tradition that is under-investigated.

Argue for a new perspective or theoretical framework.

Announce Occupation of the Research Niche/Gap

Announce the aim of the research study.

State theoretical positions/frameworks.

Describe the research design and processes.

Define key concepts and terms in the research.

4.Methodology Chapter

Functions of a Methodology Chapter

The methodology chapter details the research design and methods, including:

Describing and justifying the methodological approach.

Detailing the research design.

Justifying the specific methods for data collection.

Discussing the validity and reliability of data.

Outlining the data collection procedures.

Explaining the data analysis procedures.

Methodology Move and Sub-Move Options

Present Measurement Procedures

Overview the methodological approach.

Explain methods of measuring variables.

Justify the approach and methods.

Describe Data Collection Procedures

Describe the sample, including location, size, characteristics, context, and ethical issues.

Describe instruments used for data collection and their validity and reliability.

Detail the data collection steps.

Justify data collection procedures.

Elucidate Data and Analysis Procedures

Outline data analysis procedures.

Justify data analysis procedures.

Preview results.

5. Results Chapter

Functions of a Results Chapter

The results chapter presents and explains the findings:

Presenting results/findings relevant to research questions/hypotheses.

Explaining the meaning of findings without interpretation.

Providing evidence to support findings.

Referring back to methodology and background/context.

Referring forward to the discussion of results.

Results Move and Sub-Move Options

Present Metatextual Information

Provide background information.

Reference methodology detail.

Reference forward to discussion detail.

Link between sections.

Present Results

Restate research questions/hypotheses.

Present procedures for generating results.

Present results with evidence and explanation.

6. Discussion Chapter

Functions of a Thesis Discussion of Results

The discussion chapter interprets the results and situates them within the broader field:

Overviewing the research aims and questions/hypotheses.

Summarizing the theoretical and research contexts.

Summarizing the methodological approach.

Discussing the contribution of the results to existing theory, research, and practice.

Including interpretations, comparisons, explanations, and evaluations of the results.

Results Move and Sub-Move Options

Provide Background Information

Restate aims, research questions, and hypotheses.

Restate key published research.

Restate the research/methodological approach.

Present Statement of Results (SoR)

Restate a key result.

Expand on the key result.

Evaluate/Comment on Results or Findings

Explain the result and suggest reasons.

Comment on whether the result was expected or unexpected.

Compare results with previous research.

Provide examples of results.

Make general claims arising from the results.

Support claims with previous research.

Make recommendations for future research.

Justify further research recommendations.

7. Conclusion Chapter

Functions of a Conclusion Chapter

The conclusion chapter wraps up the thesis by:

Reminding of the aims and key methodological features.

Summarizing the study’s findings.

Evaluating the study’s contribution to theory and practice.

Discussing practical applications.

Making recommendations for further research.

Results Move and Sub-Move Options

Restatement of Aims and Methodological Approach

Restate the aims of the study.

Restate key features of the research methodology and methods.

Summary of Findings

Summarize the key findings.

Evaluation of Study’s Contribution

Discuss the significance of findings for theory and research development.

Discuss the significance of findings for practical application.

Justify the significance of findings.

Identify any limitations.

Recommendations for Further Research

Make recommendations for further research based on the findings and limitations.

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[felixcloud6288]

Fullmetal Alchemist Chapter 70

If I had to assume any one major thing in FMA was not originally planned from the start, it would be Kimblee's bodyguards. My primary bit of evidence is when we're first "introduced" to them, we only see three soldiers.

But there's a FOURTH bodyguard. The fourth one pops up randomly in the background and he's so obscure, the FMA wiki gives the wrong chapter for his first appearance. He actually makes his first appearance in a single panel this chapter.

While Kimblee is lying about his intents to help the Rockbells, he is being honest about his admiration for them and his respect for Winry as well.

I love how Ed and Kimblee have a moment of solidarity here.

In case anyone's interested in knowing, here's a list of all the mistakes Ed made while reciting the Periodic Table:

He lists Flouride instead of Flourine. Flouride is an ion of Flourine caused by Flourine gaining an additional electron

He states Sodium Carbonate, a compound which has uses in things like cleaning detergents, rather than Sodium

He jumps from Silicon (Element 14) to what is supposed to be Element 24 (This is between panels so it's not a big deal)

Instead of Chromium, Ed uses Chrome, which is what you call Chromium that has been electroplate coated over some other metal

He skips Selenium which comes after Arsenic

We know in this chapter that Smith's party had gone into the tunnel a week ago. ctually makes the timeline slightly awkward though. So now I have to re-contextualize a few things to fit this new data. I had thought the party that was wiped out was a third party that went in after Armstrong and the Elrics. But now, I think they went in sometime between chapter 66 and 67.

In chapter 67, a scout team returns to report what they've found. Then Major General Armstrong takes the Elrics into it. I'd thought the returning scout party was the entire team. But now I think only some of the scouting party came back to report their initial findings while the rest of the team went ahead.

So that means there wasn't a third expedition team that just got unlucky. There was only a single team and within 24 hours of their expedition, they delved too far and were discovered by the shadows.

And speaking of which, we now know who it is.

This is one of those reveals that feels so out of nowhere. There are some hints, primarily from the chat Wrath and Pride have in chapter 49. I mentioned it in that chapter, but there's enough hints that Pride is short.

And Pride's shadow having to leave the rescue team alone implies his shadow cannot act independently of his physical body. So when he has to be Selim, he can't guard the tunnel.

As far as Ed and Al know, Major General Armstrong has sided with Central and Briggs has fallen into their control. We know that's not the case and the scene with Roy shows us how very much that is not the case.

Major General Armstrong may absolutely hate Mustang on principle, but she does trust him with her back. Considering what she's learned about the corruption in Central, she may have chosen to contact Roy because he's the only person she can be certain is not compromised. This benefits Roy. He's been busy amassing his own pieces to play his games. He has a gun with which to shoot his opponent. Now he has a shield to defend himself.

And why would Central demand Ed's help at this point? Are they hoping to break him? Do they want to show Ed how powerless he is to stop them to the point that he has to help them instead?

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