#Custom AI Model Development

[content-ai]

Book custom AI model development service Need help to develop an AI model for your business? Book our custom AI model development service to get quality solutions for your business. Our developers can develop the model with required specifications and accuracy. Call us to book our developers now.

[technon899]

artificial intelligence| ai development companies| ai in business| ai for business automation| ai development| artificial intelligence ai| ai technology| ai companies| ai developers| ai intelligence| generative ai| ai software development| top ai companies| ai ops| ai software companies| companies that work on ai| artificial intelligence service providers in india| artificial intelligence companies| customer service ai| ai model| leading ai companies| ai in customer support| ai solutions for small business| ai for business book| basic knowledge for artificial intelligence| matching in artificial intelligence|

[rjas16]

Think Smarter, Not Harder: Meet RAG

How do RAG make machines think like you?

Imagine a world where your AI assistant doesn't only talk like a human but understands your needs, explores the latest data, and gives you answers you can trust—every single time. Sounds like science fiction? It's not.

We're at the tipping point of an AI revolution, where large language models (LLMs) like OpenAI's GPT are rewriting the rules of engagement in everything from customer service to creative writing. here's the catch: all that eloquence means nothing if it can't deliver the goods—if the answers aren't just smooth, spot-on, accurate, and deeply relevant to your reality.

The question is: Are today's AI models genuinely equipped to keep up with the complexities of real-world applications, where context, precision, and truth aren't just desirable but essential? The answer lies in pushing the boundaries further—with Retrieval-Augmented Generation (RAG).

While LLMs generate human-sounding copies, they often fail to deliver reliable answers based on real facts. How do we ensure that an AI-powered assistant doesn't confidently deliver outdated or incorrect information? How do we strike a balance between fluency and factuality? The answer is in a brand new powerful approach: Retrieval-Augmented Generation (RAG).

What is Retrieval-Augmented Generation (RAG)?

RAG is a game-changing technique to increase the basic abilities of traditional language models by integrating them with information retrieval mechanisms. RAG does not only rely on pre-acquired knowledge but actively seek external information to create up-to-date and accurate answers, rich in context. Imagine for a second what could happen if you had a customer support chatbot able to engage in a conversation and draw its answers from the latest research, news, or your internal documents to provide accurate, context-specific answers.

RAG has the immense potential to guarantee informed, responsive and versatile AI. But why is this necessary? Traditional LLMs are trained on vast datasets but are static by nature. They cannot access real-time information or specialized knowledge, which can lead to "hallucinations"—confidently incorrect responses. RAG addresses this by equipping LLMs to query external knowledge bases, grounding their outputs in factual data.

How Does Retrieval-Augmented Generation (RAG) Work?

RAG brings a dynamic new layer to traditional AI workflows. Let's break down its components:

Embedding Model

Think of this as the system's "translator." It converts text documents into vector formats, making it easier to manage and compare large volumes of data.

Retriever

It's the AI's internal search engine. It scans the vectorized data to locate the most relevant documents that align with the user's query.

Reranker (Opt.)

It assesses the submitted documents and score their relevance to guarantee that the most pertinent data will pass along.

Language Model

The language model combines the original query with the top documents the retriever provides, crafting a precise and contextually aware response. Embedding these components enables RAG to enhance the factual accuracy of outputs and allows for continuous updates from external data sources, eliminating the need for costly model retraining.

How does RAG achieve this integration?

It begins with a query. When a user asks a question, the retriever sifts through a curated knowledge base using vector embeddings to find relevant documents. These documents are then fed into the language model, which generates an answer informed by the latest and most accurate information. This approach dramatically reduces the risk of hallucinations and ensures that the AI remains current and context-aware.

RAG for Content Creation: A Game Changer or just a IT thing?

Content creation is one of the most exciting areas where RAG is making waves. Imagine an AI writer who crafts engaging articles and pulls in the latest data, trends, and insights from credible sources, ensuring that every piece of content is compelling and accurate isn't a futuristic dream or the product of your imagination. RAG makes it happen.

Why is this so revolutionary?

Engaging and factually sound content is rare, especially in today's digital landscape, where misinformation can spread like wildfire. RAG offers a solution by combining the creative fluency of LLMs with the grounding precision of information retrieval. Consider a marketing team launching a campaign based on emerging trends. Instead of manually scouring the web for the latest statistics or customer insights, an RAG-enabled tool could instantly pull in relevant data, allowing the team to craft content that resonates with current market conditions.

The same goes for various industries from finance to healthcare, and law, where accuracy is fundamental. RAG-powered content creation tools promise that every output aligns with the most recent regulations, the latest research and market trends, contributing to boosting the organization's credibility and impact.

Applying RAG in day-to-day business

How can we effectively tap into the power of RAG? Here's a step-by-step guide:

Identify High-Impact Use Cases

Start by pinpointing areas where accurate, context-aware information is critical. Think customer service, marketing, content creation, and compliance—wherever real-time knowledge can provide a competitive edge.

Curate a robust knowledge base

RAG relies on the quality of the data it collects and finds. Build or connect to a comprehensive knowledge repository with up-to-date, reliable information—internal documents, proprietary data, or trusted external sources.

Select the right tools and technologies

Leverage platforms that support RAG architecture or integrate retrieval mechanisms with existing LLMs. Many AI vendors now offer solutions combining these capabilities, so choose one that fits your needs.

Train your team

Successful implementation requires understanding how RAG works and its potential impact. Ensure your team is well-trained in deploying RAG&aapos;s technical and strategic aspects.

Monitor and optimize

Like any technology, RAG benefits from continuous monitoring and optimization. Track key performance indicators (KPIs) like accuracy, response time, and user satisfaction to refine and enhance its application.

Applying these steps will help organizations like yours unlock RAG's full potential, transform their operations, and enhance their competitive edge.

The Business Value of RAG

Why should businesses consider integrating RAG into their operations? The value proposition is clear:

Trust and accuracy

RAG significantly enhances the accuracy of responses, which is crucial for maintaining customer trust, especially in sectors like finance, healthcare, and law.

Efficiency

Ultimately, RAG reduces the workload on human employees, freeing them to focus on higher-value tasks.

Knowledge management

RAG ensures that information is always up-to-date and relevant, helping businesses maintain a high standard of knowledge dissemination and reducing the risk of costly errors.

Scalability and change

As an organization grows and evolves, so does the complexity of information management. RAG offers a scalable solution that can adapt to increasing data volumes and diverse information needs.

RAG vs. Fine-Tuning: What's the Difference?

Both RAG and fine-tuning are powerful techniques for optimizing LLM performance, but they serve different purposes:

Fine-Tuning

This approach involves additional training on specific datasets to make a model more adept at particular tasks. While effective for niche applications, it can limit the model's flexibility and adaptability.

RAG

In contrast, RAG dynamically retrieves information from external sources, allowing for continuous updates without extensive retraining, which makes it ideal for applications where real-time data and accuracy are critical.

The choice between RAG and fine-tuning entirely depends on your unique needs. For example, RAG is the way to go if your priority is real-time accuracy and contextual relevance.

Concluding Thoughts

As AI evolves, the demand for RAG AI Service Providers systems that are not only intelligent but also accurate, reliable, and adaptable will only grow. Retrieval-Augmented generation stands at the forefront of this evolution, promising to make AI more useful and trustworthy across various applications.

Whether it's a content creation revolution, enhancing customer support, or driving smarter business decisions, RAG represents a fundamental shift in how we interact with AI. It bridges the gap between what AI knows and needs to know, making it the tool of reference to grow a real competitive edge.

Let's explore the infinite possibilities of RAG together

We would love to know; how do you intend to optimize the power of RAG in your business? There are plenty of opportunities that we can bring together to life. Contact our team of AI experts for a chat about RAG and let's see if we can build game-changing models together.

[intelisync]

Generative AI for Startups: 5 Essential Boosts to Boost Your Business

The future of business growth lies in the ability to innovate rapidly, deliver personalized customer experiences, and operate efficiently. Generative AI is at the forefront of this transformation, offering startups unparalleled opportunities for growth in 2024.

Generative AI is a game-changer for startups, significantly accelerating product development by quickly generating prototypes and innovative ideas. This enables startups to innovate faster, stay ahead of the competition, and bring new products to market more efficiently. The technology also allows for a high level of customization, helping startups create highly personalized products and solutions that meet specific customer needs. This enhances customer satisfaction and loyalty, giving startups a competitive edge in their respective industries.

By automating repetitive tasks and optimizing workflows, Generative AI improves operational efficiency, saving time and resources while minimizing human errors. This allows startups to focus on strategic initiatives that drive growth and profitability. Additionally, Generative AI’s ability to analyze large datasets provides startups with valuable insights for data-driven decision-making, ensuring that their actions are informed and impactful. This data-driven approach enhances marketing strategies, making them more effective and personalized.

Intelisync offers comprehensive AI/ML services that support startups in leveraging Generative AI for growth and innovation. With Intelisync’s expertise, startups can enhance product development, improve operational efficiency, and develop effective marketing strategies. Transform your business with the power of Generative AI—Contact Intelisync today and unlock your Learn more...

[technon913]

artificial intelligence| ai development companies| ai in business| ai for business automation| ai development| artificial intelligence ai| ai technology| ai companies| ai developers| ai intelligence| generative ai| ai software development| top ai companies| ai ops| ai software companies| companies that work on ai| artificial intelligence service providers in india| artificial intelligence companies| customer service ai| ai model| leading ai companies| ai in customer support| ai solutions for small business| ai for business book| basic knowledge for artificial intelligence| matching in artificial intelligence|

[makergpt]

A Deep Dive into Generative AI Development Brilliance!

Dive into the depths of innovation with "A Deep Dive into Generative AI Development Brilliance!" This guide is your gateway to profound insights, offering a meticulous exploration of coding excellence in the world of Generative AI Development. Master the intricacies and emerge with brilliance, shaping the future of AI with every line of code! You can visit our website for more information.

[mobiloitteptyltd]

South Africa’s First AI-Powered Hospital to Open in Johannesburg In an era marked by technological advancements, South Africa is poised to make history by unveiling the continent’s inaugural AI-powered hospital, set to open its doors in Johannesburg in 2024. This pioneering leap in healthcare promises to revolutionize medical diagnosis and treatment, significantly benefiting both healthcare providers and patients. As we delve deeper into this groundbreaking development, it becomes evident that AI is not just the future of healthcare in South Africa; it’s already playing a substantial role in transforming the landscape.

[content-ai]

Hire experts for custom AI model development Want to develop an AI model for your business? Hire our experts to get custom AI model development service. Our experts can develop model with higher effectiveness and accuracy. Call us to hire our experts now. 

[technon899]

artificial intelligence| ai development companies| ai in business| ai for business automation| ai development| artificial intelligence ai| ai technology| ai companies| ai developers| ai intelligence| generative ai| ai software development| top ai companies| ai ops| ai software companies| companies that work on ai| artificial intelligence service providers in india| artificial intelligence companies| customer service ai| ai model| leading ai companies| ai in customer support| ai solutions for small business| ai for business book| basic knowledge for artificial intelligence| matching in artificial intelligence|

[makergpt]

"Your Words, Your Way: Empower Creativity with a Custom GPT Tool! 🚀🖋️"

Unlock a world of personalized language innovation with "Custom GPT Tool" – where your words take center stage! 🌐💬

🎨 Tailor your narrative and elevate your storytelling with a tool that allows you to customize the language model to your unique style. "Custom GPT Tool" is not just about coding; it's about infusing your creative fingerprint into every word, turning your ideas into linguistic masterpieces. ✨💻

Craft brilliance effortlessly, whether you're a seasoned developer or a writing enthusiast. This tool empowers you to shape the future of communication, putting your words at the forefront of language innovation. 🌟📖

Join the revolution of personalized language models, and let your creativity flow in a digital realm where every sentence reflects your individuality. Your words, your way – the journey with "Custom GPT Tool" promises to redefine how we communicate in the ever-evolving landscape of artificial intelligence. 🤖🔍 #CustomGPT #LanguageInnovation #CreativeCoding

[avantaritechnologies]

Koi Feature Demo

For Avantari Website. Please visit: www.avantari.org

[content-ai]

Book custom AI model development service Want to develop an AI model for your business? Book our custom AI model development service to get a model with the right accuracy and effectiveness. Our developers can customized the model with the right features and specifications. Call us to book our services now. 

[mostlysignssomeportents]

Google’s enshittification memos

[Note, 9 October 2023: Google disputes the veracity of this claim, but has declined to provide the exhibits and testimony to support its claims. Read more about this here.]

When I think about how the old, good internet turned into the enshitternet, I imagine a series of small compromises, each seemingly reasonable at the time, each contributing to a cultural norm of making good things worse, and worse, and worse.

Think about Unity President Marc Whitten's nonpology for his company's disastrous rug-pull, in which they declared that everyone who had paid good money to use their tool to make a game would have to keep paying, every time someone downloaded that game:

The most fundamental thing that we’re trying to do is we’re building a sustainable business for Unity. And for us, that means that we do need to have a model that includes some sort of balancing change, including shared success.

https://www.wired.com/story/unity-walks-back-policies-lost-trust/

"Shared success" is code for, "If you use our tool to make money, we should make money too." This is bullshit. It's like saying, "We just want to find a way to share the success of the painters who use our brushes, so every time you sell a painting, we want to tax that sale." Or "Every time you sell a house, the company that made the hammer gets to wet its beak."

And note that they're not talking about shared risk here – no one at Unity is saying, "If you try to make a game with our tools and you lose a million bucks, we're on the hook for ten percent of your losses." This isn't partnership, it's extortion.

How did a company like Unity – which became a market leader by making a tool that understood the needs of game developers and filled them – turn into a protection racket? One bad decision at a time. One rationalization and then another. Slowly, and then all at once.

When I think about this enshittification curve, I often think of Google, a company that had its users' backs for years, which created a genuinely innovative search engine that worked so well it seemed like *magic, a company whose employees often had their pick of jobs, but chose the "don't be evil" gig because that mattered to them.

People make fun of that "don't be evil" motto, but if your key employees took the gig because they didn't want to be evil, and then you ask them to be evil, they might just quit. Hell, they might make a stink on the way out the door, too:

https://theintercept.com/2018/09/13/google-china-search-engine-employee-resigns/

Google is a company whose founders started out by publishing a scientific paper describing their search methodology, in which they said, "Oh, and by the way, ads will inevitably turn your search engine into a pile of shit, so we're gonna stay the fuck away from them":

http://infolab.stanford.edu/pub/papers/google.pdf

Those same founders retained a controlling interest in the company after it went IPO, explaining to investors that they were going to run the business without having their elbows jostled by shortsighted Wall Street assholes, so they could keep it from turning into a pile of shit:

https://abc.xyz/investor/founders-letters/ipo-letter/

And yet, it's turned into a pile of shit. Google search is so bad you might as well ask Jeeves. The company's big plan to fix it? Replace links to webpages with florid paragraphs of chatbot nonsense filled with a supremely confident lies:

https://pluralistic.net/2023/05/14/googles-ai-hype-circle/

How did the company get this bad? In part, this is the "curse of bigness." The company can't grow by attracting new users. When you have 90%+ of the market, there are no new customers to sign up. Hypothetically, they could grow by going into new lines of business, but Google is incapable of making a successful product in-house and also kills most of the products it buys from other, more innovative companies:

https://killedbygoogle.com/

Theoretically, the company could pursue new lines of business in-house, and indeed, the current leaders of companies like Amazon, Microsoft and Apple are all execs who figured out how to get the whole company to do something new, and were elevated to the CEO's office, making each one a billionaire and sealing their place in history.

It is for this very reason that any exec at a large firm who tries to make a business-wide improvement gets immediately and repeatedly knifed by all their colleagues, who correctly reason that if someone else becomes CEO, then they won't become CEO. Machiavelli was an optimist:

https://pluralistic.net/2023/07/28/microincentives-and-enshittification/

With no growth from new customers, and no growth from new businesses, "growth" has to come from squeezing workers (say, laying off 12,000 engineers after a stock buyback that would have paid their salaries for the next 27 years), or business customers (say, by colluding with Facebook to rig the ad market with the Jedi Blue conspiracy), or end-users.

Now, in theory, we might never know exactly what led to the enshittification of Google. In theory, all of compromises, debates and plots could be lost to history. But tech is not an oral culture, it's a written one, and techies write everything down and nothing is ever truly deleted.

Time and again, Big Tech tells on itself. Think of FTX's main conspirators all hanging out in a group chat called "Wirefraud." Amazon naming its program targeting weak, small publishers the "Gazelle Project" ("approach these small publishers the way a cheetah would pursue a sickly gazelle”). Amazon documenting the fact that users were unknowingly signing up for Prime and getting pissed; then figuring out how to reduce accidental signups, then deciding not to do it because it liked the money too much. Think of Zuck emailing his CFO in the middle of the night to defend his outsized offer to buy Instagram on the basis that users like Insta better and Facebook couldn't compete with them on quality.

It's like every Big Tech schemer has a folder on their desktop called "Mens Rea" filled with files like "Copy_of_Premeditated_Murder.docx":

https://doctorow.medium.com/big-tech-cant-stop-telling-on-itself-f7f0eb6d215a?sk=351f8a54ab8e02d7340620e5eec5024d

Right now, Google's on trial for its sins against antitrust law. It's a hard case to make. To secure a win, the prosecutors at the DoJ Antitrust Division are going to have to prove what was going on in Google execs' minds when the took the actions that led to the company's dominance. They're going to have to show that the company deliberately undertook to harm its users and customers.

Of course, it helps that Google put it all in writing.

Last week, there was a huge kerfuffile over the DoJ's practice of posting its exhibits from the trial to a website each night. This is a totally normal thing to do – a practice that dates back to the Microsoft antitrust trial. But Google pitched a tantrum over this and said that the docs the DoJ were posting would be turned into "clickbait." Which is another way of saying, "the public would find these documents very interesting, and they would be damning to us and our case":

https://www.bigtechontrial.com/p/secrecy-is-systemic

After initially deferring to Google, Judge Amit Mehta finally gave the Justice Department the greenlight to post the document. It's up. It's wild:

https://www.justice.gov/d9/2023-09/416692.pdf

The document is described as "notes for a course on communication" that Google VP for Finance Michael Roszak prepared. Roszak says he can't remember whether he ever gave the presentation, but insists that the remit for the course required him to tell students "things I didn't believe," and that's why the document is "full of hyperbole and exaggeration."

OK.

But here's what the document says: "search advertising is one of the world's greatest business models ever created…illicit businesses (cigarettes or drugs) could rival these economics…[W]e can mostly ignore the demand side…(users and queries) and only focus on the supply side of advertisers, ad formats and sales."

It goes on to say that this might be changing, and proposes a way to balance the interests of the search and ads teams, which are at odds, with search worrying that ads are pushing them to produce "unnatural search experiences to chase revenue."

"Unnatural search experiences to chase revenue" is a thinly veiled euphemism for the prophetic warnings in that 1998 Pagerank paper: "The goals of the advertising business model do not always correspond to providing quality search to users." Or, more plainly, "ads will turn our search engine into a pile of shit."

And, as Roszak writes, Google is "able to ignore one of the fundamental laws of economics…supply and demand." That is, the company has become so dominant and cemented its position so thoroughly as the default search engine across every platforms and system that even if it makes its search terrible to goose revenues, users won't leave. As Lily Tomlin put it on SNL: "We don't have to care, we're the phone company."

In the enshittification cycle, companies first lure in users with surpluses – like providing the best search results rather than the most profitable ones – with an eye to locking them in. In Google's case, that lock-in has multiple facets, but the big one is spending billions of dollars – enough to buy a whole Twitter, every single year – to be the default search everywhere.

Google doesn't buy its way to dominance because it has the very best search results and it wants to shield you from inferior competitors. The economically rational case for buying default position is that preventing competition is more profitable than succeeding by outperforming competitors. The best reason to buy the default everywhere is that it lets you lower quality without losing business. You can "ignore the demand side, and only focus on advertisers."

For a lot of people, the analysis stops here. "If you're not paying for the product, you're the product." Google locks in users and sells them to advertisers, who are their co-conspirators in a scheme to screw the rest of us.

But that's not right. For one thing, paying for a product doesn't mean you won't be the product. Apple charges a thousand bucks for an iPhone and then nonconsensually spies on every iOS user in order to target ads to them (and lies about it):

https://pluralistic.net/2022/11/14/luxury-surveillance/#liar-liar

John Deere charges six figures for its tractors, then runs a grift that blocks farmers from fixing their own machines, and then uses their control over repair to silence farmers who complain about it:

https://pluralistic.net/2022/05/31/dealers-choice/#be-a-shame-if-something-were-to-happen-to-it

Fair treatment from a corporation isn't a loyalty program that you earn by through sufficient spending. Companies that can sell you out, will sell you out, and then cry victim, insisting that they were only doing their fiduciary duty for their sacred shareholders. Companies are disciplined by fear of competition, regulation or – in the case of tech platforms – customers seizing the means of computation and installing ad-blockers, alternative clients, multiprotocol readers, etc:

https://doctorow.medium.com/an-audacious-plan-to-halt-the-internets-enshittification-and-throw-it-into-reverse-3cc01e7e4604?sk=85b3f5f7d051804521c3411711f0b554

Which is where the next stage of enshittification comes in: when the platform withdraws the surplus it had allocated to lure in – and then lock in – business customers (like advertisers) and reallocate it to the platform's shareholders.

For Google, there are several rackets that let it screw over advertisers as well as searchers (the advertisers are paying for the product, and they're also the product). Some of those rackets are well-known, like Jedi Blue, the market-rigging conspiracy that Google and Facebook colluded on:

https://en.wikipedia.org/wiki/Jedi_Blue

But thanks to the antitrust trial, we're learning about more of these. Megan Gray – ex-FTC, ex-DuckDuckGo – was in the courtroom last week when evidence was presented on Google execs' panic over a decline in "ad generating searches" and the sleazy gimmick they came up with to address it: manipulating the "semantic matching" on user queries:

https://www.wired.com/story/google-antitrust-lawsuit-search-results/

When you send a query to Google, it expands that query with terms that are similar – for example, if you search on "Weds" it might also search for "Wednesday." In the slides shown in the Google trial, we learned about another kind of semantic matching that Google performed, this one intended to turn your search results into "a twisted shopping mall you can’t escape."

Here's how that worked: when you ran a query like "children's clothing," Google secretly appended the brand name of a kids' clothing manufacturer to the query. This, in turn, triggered a ton of ads – because rival brands will have bought ads against their competitors' name (like Pepsi buying ads that are shown over queries for Coke).

Here we see surpluses being taken away from both end-users and business customers – that is, searchers and advertisers. For searchers, it doesn't matter how much you refine your query, you're still going to get crummy search results because there's an unkillable, hidden search term stuck to your query, like a piece of shit that Google keeps sticking to the sole of your shoe.

But for advertisers, this is also a scam. They're paying to be matched to users who search on a brand name, and you didn't search on that brand name. It's especially bad for the company whose name has been appended to your search, because Google has a protection racket where the company that matches your search has to pay extra in order to show up overtop of rivals who are worse matches. Both the matching company and those rivals have given Google a credit-card that Google gets to bill every time a user searches on the company's name, and Google is just running fraudulent charges through those cards.

And, of course, Google put this in writing. I mean, of course they did. As we learned from the documentary The Incredibles, supervillains can't stop themselves from monologuing, and in big, sprawling monopolists, these monologues have to transmitted electronically – and often indelibly – to far-flung co-cabalists.

As Gray points out, this is an incredibly blunt enshittification technique: "it hadn’t even occurred to me that Google just flat out deletes queries and replaces them with ones that monetize better." We don't know how long Google did this for or how frequently this bait-and-switch was deployed.

But if this is a blunt way of Google smashing its fist down on the scales that balance search quality against ad revenues, there's plenty of subtler ways the company could sneak a thumb on there. A Google exec at the trial rhapsodized about his company's "contract with the user" to deliver an "honest results policy," but given how bad Google search is these days, we're left to either believe he's lying or that Google sucks at search.

The paper trail offers a tantalizing look at how a company went from doing something that was so good it felt like a magic trick to being "able to ignore one of the fundamental laws of economics…supply and demand," able to "ignore the demand side…(users and queries) and only focus on the supply side of advertisers."

What's more, this is a system where everyone loses (except for Google): this isn't a grift run by Google and advertisers on users – it's a grift Google runs on everyone.

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2023/10/03/not-feeling-lucky/#fundamental-laws-of-economics

My next novel is The Lost Cause, a hopeful novel of the climate emergency. Amazon won't sell the audiobook, so I made my own and I'm pre-selling it on Kickstarter!

[rainy-daze1]

just so people know 👍

transcript:

Everyone else has been getting on with the NPCs.

Um, by the way guys, people have had questions on the way the AI actually works in this game. From what I'm understanding, from what the developer has said, this is a custom model that the developer himself has made. So this is not any one of those, like, it's not ChatGPT, it's not Gemini. It's a custom model built specifically for the mod. So I know people have had questions on that.