#LLM SEO content

[seoladyltd]

Google UK Featured Snippet SEO Blog post Case Study 2023 September Nina Payne White Label Freelancer

#googledance #googlecore #googlealgorithm #featuredsnippet https://www.seolady.co.uk/google-dance-core-update/

September 7th, 2023 - Google’s Update has finally concluded after it's koolaid smash entry in August. I created a new blog post in August 2023 so I could update the article in real time twice a week. I was aiming for a featured snippet in SERPs for a low competition Featured Snippet at the top of Google page 1, so you can understand what on-page SEO methods you'll need to plan. Also, timescales; featured snippets can take months and years of chasing before you hit the sweet ranking spot and enjoy extra traffic. This core update, confirmed by Google to have completed rolling out on September 7th, took over two weeks to fully implement. Volatility was seen in waves, with initial fluctuations around August 25th, another surge near August 30th, and final volatility in the days before it ended. As with previous updates focused on Google’s core ranking systems, this latest August 2023 core update targeted all types of content and languages worldwide. It aims to reward high-quality, useful content while downgrading low-value pages. Sites generally see ranking increases or decreases of 20-80% or more. While Google continues to release algorithm changes, they changed from the old dancing way of flicking a switch; the gradual rollout method used in 2023 has the purpose of reducing large fluctuations and frustrations. This is to combat an overnight domain bombing in search, like in the twenties with the first introduction of E-A-T and HTTPS-favoured urls in their algorithm changes. The volatility of Google’s search results in the early days of the search engine, was caused by a number of factors – including the fact that Google’s algorithm was still under development and that the search engine was constantly being updated. The Google Dance was a major source of frustration for website owners and SEO professionals. It was difficult to predict how a website’s ranking would change, and it was often impossible to know why a website’s ranking had changed, in the mid-2000s it levelled out as Google’s algorithm became more sophisticated. However, the term “Google Dance” is still used today to refer to any sudden or unexplained changes in Google’s search results from older generation of online marketers. The phrase became more widely used in 2004, when Google’s algorithm was updated and the Google Dance became more pronounced. The Google Dance began to subside in the mid-2000s, but the phrase is still used today to refer to any sudden or unexplained changes in Google’s search results.

[txttletale]

like, i said this in my longer post on this but i think it bears repeating on its own because it really helps to understand the whole dynamic at play here: talk to anyone with experience working in yknow, the greater buzzfeed content slop industry, and they will tell you that before LLMs came along, it was already one robot (whatever SEO optimization tool your workplace paid for) writing for another robot (google search algorithm) -- with any human writers serving primarily as a middleman between the two

[imsobadatnicknames2]

How can you consider yourself any sort of leftist when you defend AI art bullshit? You literally simp for AI techbros and have the gall to pretend you're against big corporations?? Get fucked

I don't "defend" AI art. I think a particular old post of mine that a lot of people tend to read in bad faith must be making the rounds again lmao.

Took me a good while to reply to this because you know what? I decided to make something positive out of this and use this as an opportunity to outline what I ACTUALLY believe about AI art. If anyone seeing this decides to read it in good or bad faith... Welp, your choice I guess.

I have several criticisms of the way the proliferation of AI art generators and LLMs is making a lot of things worse. Some of these are things I have voiced in the past, some of these are things I haven't until now:

Most image and text AI generators are fine-tuned to produce nothing but the most agreeable, generically pretty content slop, pretty much immediately squandering their potential to be used as genuinely interesting artistic tools with anything to offer in terms of a unique aesthetic experience (AI video still manages to look bizarre and interesting but it's getting there too)

In the entertainment industry and a lot of other fields, AI image generation is getting incorporated into production pipelines in ways that lead to the immiseration of working artists, being used to justify either lower wages or straight-up layoffs, and this is something that needs to be fought against. That's why I unconditionally supported the SAG-AFTRA strikes last year and will unconditionally support any collective action to address AI art as a concrete labor issue

In most fields where it's being integrated, AI art is vastly inferior to human artists in any use case where you need anything other than to make a superficially pretty picture really fast. If you need to do anything like ask for revisions or minor corrections, give very specific descriptions of how objects and people are interacting with each other, or just like. generate several pictures of the same thing and have them stay consistent with each other, you NEED human artists and it's preposterous to think they can be replaced by AI.

There is a lot of art on the internet that consists of the most generically pretty, cookie-cutter anime waifu-adjacent slop that has zero artistic or emotional value to either the people seeing it or the person churning it out, and while this certainly was A Thing before the advent of AI art generators, generative AI has made it extremely easy to become the kind of person who churns it out and floods online art spaces with it.

Similarly, LLMs make it extremely easy to generate massive volumes of texts, pages, articles, listicles and what have you that are generic vapid SEO-friendly pap at best and bizzarre nonsense misinformation at worst, drowning useful information in a sea of vapid noise and rendering internet searches increasingly useless.

The way LLMs are being incorporated into customer service and similar services not only, again, encourages further immiseration of customer service workers, but it's also completely useless for most customers.

A very annoyingly vocal part the population of AI art enthusiasts, fanatics and promoters do tend to talk about it in a way that directly or indirectly demeans the merit and skill of human artists and implies that they think of anyone who sees anything worthwile in the process of creation itself rather than the end product as stupid or deluded.

So you can probably tell by now that I don't hold AI art or writing in very high regard. However (and here's the part that'll get me called an AI techbro, or get people telling me that I'm just jealous of REAL artists because I lack the drive to create art of my own, or whatever else) I do have some criticisms of the way people have been responding to it, and have voiced such criticisms in the past.

I think a lot of the opposition to AI art has critstallized around unexamined gut reactions, whipping up a moral panic, and pressure to outwardly display an acceptable level of disdain for it. And in particular I think this climate has made a lot of people very prone to either uncritically entertain and adopt regressive ideas about Intellectual Propety, OR reveal previously held regressive ideas about Intellectual Property that are now suddenly more socially acceptable to express:

(I wanna preface this section by stating that I'm a staunch intellectual property abolitionist for the same reason I'm a private property abolitionist. If you think the existence of intellectual property is a good thing, a lot of my ideas about a lot of stuff are gonna be unpalatable to you. Not much I can do about it.)

A lot of people are suddenly throwing their support behind any proposal that promises stricter copyright regulations to combat AI art, when a lot of these also have the potential to severely udnermine fair use laws and fuck over a lot of independent artist for the benefit of big companies.

It was very worrying to see a lot of fanfic authors in particular clap for the George R R Martin OpenAI lawsuit because well... a lot of them don't realize that fanfic is a hobby that's in a position that's VERY legally precarious at best, that legally speaking using someone else's characters in your fanfic is as much of a violation of copyright law as straight up stealing entire passages, and that any regulation that can be used against the latter can be extended against the former.

Similarly, a lot of artists were cheering for the lawsuit against AI art models trained to mimic the style of specific artists. Which I agree is an extremely scummy thing to do (just like a human artist making a living from ripping off someone else's work is also extremely scummy), but I don't think every scummy act necessarily needs to be punishable by law, and some of them would in fact leave people worse off if they were. All this to say: If you are an artist, and ESPECIALLY a fan artist, trust me. You DON'T wanna live in a world where there's precedent for people's artstyles to be considered intellectual property in any legally enforceable way. I know you wanna hurt AI art people but this is one avenue that's not worth it.

Especially worrying to me as an indie musician has been to see people mention the strict copyright laws of the music industry as a positive thing that they wanna emulate. "this would never happen in the music industry because they value their artists copyright" idk maybe this is a the grass is greener type of situation but I'm telling you, you DON'T wanna live in a world where copyright law in the visual arts world works the way it does in the music industry. It's not worth it.

I've seen at least one person compare AI art model training to music sampling and say "there's a reason why they cracked down on sampling" as if the death of sampling due to stricter copyright laws was a good thing and not literally one of the worst things to happen in the history of music which nearly destroyed several primarily black music genres. Of course this is anecdotal because it's just One Guy I Saw Once, but you can see what I mean about how uncritical support for copyright law as a tool against AI can lead people to adopt increasingly regressive ideas about copyright.

Similarly, I've seen at least one person go "you know what? Collages should be considered art theft too, fuck you" over an argument where someone else compared AI art to collages. Again, same point as above.

Similarly, I take issue with the way a lot of people seem EXTREMELY personally invested in proving AI art is Not Real Art. I not only find this discussion unproductive, but also similarly dangerously prone to validating very reactionary ideas about The Nature Of Art that shouldn't really be entertained. Also it's a discussion rife with intellectual dishonesty and unevenly applied definition and standards.

When a lot of people present the argument of AI art not being art because the definition of art is this and that, they try to pretend that this is the definition of art the've always operated under and believed in, even when a lot of the time it's blatantly obvious that they're constructing their definition on the spot and deliberately trying to do so in such a way that it doesn't include AI art.

They never succeed at it, btw. I've seen several dozen different "AI art isn't art because art is [definition]". I've seen exactly zero of those where trying to seriously apply that definition in any context outside of trying to prove AI art isn't art doesn't end up in it accidentally excluding one or more non-AI artforms, usually reflecting the author's blindspots with regard to the different forms of artistic expression.

(However, this is moot because, again, these are rarely definitions that these people actually believe in or adhere to outside of trying to win "Is AI art real art?" discussions.)

Especially worrying when the definition they construct is built around stuff like Effort or Skill or Dedication or The Divine Human Spirit. You would not be happy about the kinds of art that have traditionally been excluded from Real Art using similar definitions.

Seriously when everyone was celebrating that the Catholic Church came out to say AI art isn't real art and sharing it as if it was validating and not Extremely Worrying that the arguments they'd been using against AI art sounded nearly identical to things TradCaths believe I was like. Well alright :T You can make all the "I never thought I'd die fighting side by side with a catholic" legolas and gimli memes you want, but it won't change the fact that the argument being made by the catholic church was a profoundly conservative one and nearly identical to arguments used to dismiss the artistic merit of certain forms of "degenerate" art and everyone was just uncritically sharing it, completely unconcerned with what kind of worldview they were lending validity to by sharing it.

Remember when the discourse about the Gay Sex cats pic was going on? One of the things I remember the most from that time was when someone went "Tell me a definition of art that excludes this picture without also excluding Fountain by Duchamp" and how just. Literally no one was able to do it. A LOT of people tried to argue some variation of "Well, Fountain is art and this image isn't because what turns fountain into art is Intent. Duchamp's choice to show a urinal at an art gallery as if it was art confers it an element of artistic intent that this image lacks" when like. Didn't by that same logic OP's choice to post the image on tumblr as if it was art also confer it artistic intent in the same way? Didn't that argument actually kinda end up accidentally validating the artistic status of every piece of AI art ever posted on social media? That moment it clicked for me that a lot of these definitions require applying certain concepts extremely selectively in order to make sense for the people using them.

A lot of people also try to argue it isn't Real Art based on the fact that most AI art is vapid but like. If being vapid definitionally excludes something from being art you're going to have to exclude a whooole lot of stuff along with it. AI art is vapid. A lot of art is too, I don't think this argument works either.

Like, look, I'm not really invested in trying to argue in favor of The Artistic Merits of AI art but I also find it extremely hard to ignore how trying to categorically define AI art as Not Real Art not only is unproductive but also requires either a) applying certain parts of your definition of art extremely selectively, b) constructing a definition of art so convoluted and full of weird caveats as to be functionally useless, or c) validating extremely reactionary conservative ideas about what Real Art is.

Some stray thoughts that don't fit any of the above sections.

I've occassionally seen people respond to AI art being used for shitposts like "A lot of people have affordable commissions, you could have paid someone like $30 to draw this for you instead of using the plagiarism algorithm and exploiting the work of real artists" and sorry but if you consider paying an artist a rate that amounts to like $5 for several hours of work a LESS exploitative alternative I think you've got something fucked up going on with your priorities.

Also it's kinda funny when people comment on the aforementioned shitposts with some variation of "see, the usage of AI art robs it of all humor because the thing that makes shitposts funny is when you consider the fact that someone would spend so much time and effort in something so stupid" because like. Yeah that is part of the humor SOMETIMES but also people share and laugh at low effort shitposts all the time. Again you're constructing a definition that you don't actually believe in anywhere outside of this type of conversations. Just say you don't like that it's AI art because you think it's morally wrong and stop being disingenuous.

So yeah, this is pretty much everything I believe about the topic.

I don't "defend" AI art, but my opposition to it is firmly rooted in my principles, and that means I refuse to uncritically accept any anti-AI art argument that goes against those same principles.

If you think not accepting and parroting every Anti-AI art argument I encounter because some of them are ideologically rooted in things I disagree with makes me indistinguishable from "AI techbros" you're working under a fucked up dichotomy.

[mostlysignssomeportents]

The economics of AI spam and what it means for post-AI bubble spammers

A persistent current in the people who believe that AI could be profitable from low-risk activities is the belief that people don't care about extremely low-value AI-generated spam, and that this spam generates a lot of money.

These are both totally incorrect. The point of AI generated spam is to get clicks people who are looking for better content. It's SEO. No one reads 2000 words of algorithm-pleasiing LLM garbage over an omelette recipe and then subscribes to that site's feed.

And the omelette recipe generates pennies for the spammer that posted it. They are doing massive volume in order to make those pennies into dollars. You don't make money by posting one spam. If every spammer had to pay the actual recovery costs (energy, chillers, capital amortization, wages) for their query, every AI-generated spam would lose (lots of) money.

[hackeocafe]

How to use ChatGPT in 2024 full tutorial

Begin your journey to being a ChatGPT Pro with our 12-hour ChatGPT Masterclass. This video covers everything from basics to advanced, starting with the fundamentals of ChatGPT, Generative AI, and Large Language Models (LLMs). You'll learn how to navigate ChatGPT's interface, delve into Prompt Engineering, and master effective prompting strategies. We introduce different ChatGPT versions (3.5, 4, 4o), their differences, and usage. You'll build programs, handle exceptions, test codes, and create Python apps and websites using ChatGPT 4o. Additionally, you'll analyze data with Python and Excel, simplify tasks in Excel and PowerPoint, create diverse content, and use ChatGPT for SEO, digital marketing, and finance. Finally, learn to create custom GPTs tailored to your needs

[theagileoperator]

"SEO is no longer sufficient. AIO, (AI-optimization), will become necessary to ensure the results are accurate."

So, how do you do AI-optimization? Who better to ask than an LLM? ChatGPT advises the following:

Anchor Your Online Presence with Accuracy: Make your official website and social media platforms repositories of precise, up-to-date product information.

Implement Structured Data: Use schema markup to help search engines and indirectly, LLMs, better understand and accurately represent your products.

Engage on Review Platforms and Q&A Sites: Actively manage product listings and engage in discussions to ensure accurate information proliferates.

Leverage Press Releases: Regularly release official statements to keep the information about your products current and easily accessible.

Partner with Content Creators: Collaborate with influencers and content creators to amplify accurate product narratives.

Offer Feedback to Model Developers: Participate in improving LLM accuracy by providing feedback on misinformation directly to developers, where possible.

Monitor Your Brand: Use digital tools to keep an eye on how your products are being discussed online.

Produce Quality Content: Distribute educational and informational content that accurately reflects your products' value and features.

Engage in Industry Discussions: Share your expertise in forums and professional networks to influence the narrative accurately.

Consider Legal Measures for Misinformation: As a last resort, take legal action to correct significant misinformation that could harm your brand.

In the dynamic realm of AI, proactivity in managing our online presence is not just beneficial; it's essential. Let's lead the charge in ensuring technology enhances our brands accurately and responsibly. #AI #SEO #ChatGPT #SocialMediaEngagement #Business #Online #TheAgileOperator

[ivan-fyodorovich-k]

ChatGPT’s success at mimicking human language is an indictment of the ways we already use words. LLMs are the technology that a culture awash in prepared and manipulative content deserves. It’s a small step from employees spinning out endless SEO (search engine optimization) content and social media influencers chasing eyeballs to LLM-written text and deep fakes. An Equity, Diversity, and Inclusion office recently sent out a ChatGPT-generated email after a shooting at another school. Such bureaucratic emails are already formulaic and essentially meaningless, and the fact that an LLM could generate a passable email exposes the pre-existing vapidity of this discourse.

. . .

While much of the hype around ChatGPT lauds its productivity gains and egalitarian possibilities, the actual results will be more likely to exacerbate inequalities. Those who control these tools will profit; those who are on the receiving end of them will be left to make their way in a degraded intellectual ecosystem.

Take, for instance, the statements of a key leader who made the decision to release ChatGPT publicly last year. OpenAI CEO Sam Altman sounds as if he’s concerned, not merely for corporate profit, but for the common good. As one Wall Street Journal article reports, “he fears what could happen if AI is rolled out into society recklessly.” Yet he overruled the concerns of his own employees that the decision to publicly release ChatGPT would be reckless, and he insists that the way to get AI “right is to have people engage with it, explore these systems, study them, to learn how to make them safe.” In other words, he treats human society as a vast laboratory of caged guinea pigs, with no concern for how they might be harmed by his social experiment. As Alan Jacobs points out, Altman’s attitude isn’t altruistic but sociopathic. It’s not surprising, then, that his technology also displays sociopathic tendencies.

Several people I’ve shared these concerns with have cited the possibilities for ChatGPT to help individuals for whom English is a second language or people with language disabilities. Or they’ve suggested it might serve as a personal tutor for kids who need extra help and don’t have a teacher who can give them the time they need. In this latter case, however, what is “solved” is not the needs of the student but the effort required to care for a student; if a student is patiently helped by an adult, the student is being told that she matters, that she is worth the time and attention of another person. If this student instead receives answers automatically generated by a computer, she is being told she’s not worth someone’s time. The medium is the message.

. . .

We cannot blithely adopt LLMs without becoming complicit in their Faustian bargain and without, as Berry warns, enfranchising our exploiters. Neither, however, can we pretend that we live in a world where these technologies do not exist. And I have no expectation that the development of these AIs will slow down or be conducted with real care for their externalized cultural costs. There’s simply too much easy money to be made selling shoddy, knock off performances of “intelligence.” Berry writes that “it is easy for me to imagine that the next great division of the world will be between people who wish to live as creatures and people who wish to live as machines.” As has been clear for quite some time, even those of us who want to live as creatures will have to figure out how to do so in a world designed and manufactured by those who prefer to live as machines. LLMs create “no absolutely new situation;” they are simply the latest reminder – the most recent apocalypse – of the technopoly we have long been building.

To live as creatures in a world built for machines, we will need to patiently and creatively make do. I get this phrase “making do” from the French Catholic writer Michel de Certeau who describes the possibilities that people have in their everyday lives to find ways that creatively resist or subvert inhuman systems. Practicing the productive effort that develops our capacities as free persons, made in the image of God, will not be easy in the world we find ourselves. But it remains possible.

[ms-demeanor]

Windows Central:

Forbes:

AWS researchers:

The Nature study theoretically being described in the first story has the number "57" in it twice, once a an edition number and once as a page number in the references, and is about model collapse that could theoretically occur if LLMs are trained on LLM-generated content.

In the study they say that data poisoning is not new and we see examples of it in content farms and SEO bait bullshit, but that there is reason for concern because LLMs can produce so much bullshit so quickly, and I think that really underestimates the kind and volume of bullshit that good old fashioned humans like the writers at Windows Central and Forbes are capable of churning out.

A news site called WindowsCentral just posted a headline: "57% of all content on the web is AI-generated."

They're misquoting a Forbes article that said, "57% of all text-based content on the web is AI-generated."

Which itself was also a misquote of a study saying "57% of all text translations on the web are machine generated."

Figured I should give everyone a heads up

for all the "OMG dead Internet theory is real!" posting coming up.

[aidesignemea]

LLM SEO EXPERT

LLM Applications in SEO

I'm observing how LLMs are being applied across various SEO functions. In content creation, they assist with drafting, outlining, optimizing titles and meta descriptions, and validating content quality based on E-E-A-T principles. For keyword research, LLMs are moving beyond simple keyword matching to understanding search intent and context, helping to identify long-tail keywords and trending topics. In technical SEO, they aid with schema markup, site architecture simplification, voice search optimization, and identifying technical issues like broken links. A new concept, 'LLM optimization' (LLMO) or 'AI Engine Optimization' (AEO), is emerging, focusing on content discoverability across conversational AI tools.

[aiandblockchainchronicles]

The Rise of Custom LLMs: Why Enterprises Are Moving Beyond Off-the-Shelf AI

In the rapidly evolving landscape of artificial intelligence, one trend stands out among forward-thinking companies: the rising investment in custom Large Language Model (LLM) development services. From automating internal workflows to enhancing customer experience and driving innovation, custom LLMs are becoming indispensable tools for enterprises aiming to stay competitive in 2025 and beyond.

While off-the-shelf LLM solutions like ChatGPT and Claude offer powerful capabilities, they often fall short when it comes to aligning with an organization’s specific goals, data needs, and compliance requirements. This gap has led many businesses to pursue tailor-made LLM development services that are optimized for their unique workflows, industry demands, and long-term strategies.

Let’s explore why custom LLM development is now a critical investment for companies across industries—and what it means for the future of AI adoption.

The Limitations of Off-the-Shelf LLMs

Off-the-shelf LLMs are great for general use cases, but they come with constraints that limit enterprise adoption in certain domains. These models are trained on public datasets, and while they can provide impressive general language capabilities, they struggle with:

Domain-specific knowledge: They often lack context around specialized terminology in industries like legal, finance, or healthcare.

Data privacy concerns: Public LLMs may not meet internal data handling, security, or compliance needs.

Limited control: Companies can’t fine-tune or extend model capabilities to fit proprietary processes or logic.

Generic outputs: The lack of personalization often leads to low relevance or alignment with brand tone, customer context, or specific operational workflows.

As companies scale their AI efforts, they need models that deeply understand their operations and can evolve with them. This is where custom LLM development services enter the picture.

Custom LLMs: Tailored to Fit Business Needs

Custom LLM development allows organizations to build models that are trained on their own datasets, integrate seamlessly with existing software, and address very specific tasks. These LLMs can be designed to follow particular business rules, support multilingual requirements, and deliver high-precision results for mission-critical applications.

Personalized Workflows

A custom LLM can be tailored to handle unique workflows—whether it’s analyzing thousands of legal contracts, generating hyper-personalized marketing copy, or assisting customer service agents with real-time knowledge retrieval. This kind of specificity is nearly impossible with generic LLMs.

Enhanced Performance

When trained on proprietary datasets, custom LLMs significantly outperform general models on niche use cases. They understand organizational terminology, workflows, and customer interactions—leading to better predictions, more accurate outputs, and ultimately, higher productivity.

Seamless Integration

Custom models can be built to integrate tightly with a company’s tech stack—ERP systems, CRMs, document repositories, and even internal APIs. This allows companies to deploy LLMs not just as standalone assistants, but as core components of digital infrastructure.

Key Business Drivers Behind the Investment

Let’s look at the top reasons why companies are increasingly turning to custom LLM development services in 2025.

1. Competitive Differentiation

AI is no longer a buzzword—it’s a key differentiator. Companies that leverage LLMs to create unique customer experiences or optimize operations gain a real edge. With a custom LLM, businesses can offer features or services that competitors using standard models simply can’t match.

For example, a custom-trained LLM in eCommerce could generate personalized product descriptions, SEO content, and chat support tailored to a brand’s voice and buyer personas—something a general LLM cannot consistently deliver.

2. Data Ownership and Security

As data regulations like GDPR, HIPAA, and India’s DPDP Act become stricter, companies must be cautious about how and where their data is processed. Custom LLMs, especially those developed for on-premises or private cloud environments, offer full data control.

This ensures sensitive internal data doesn’t leave the company’s infrastructure while still allowing AI to add value. It also reduces dependency on external SaaS vendors, mitigating third-party risk.

3. Cost Efficiency at Scale

Although custom LLM development requires an initial investment, it becomes more cost-effective at scale. Businesses no longer pay per token or API call to third-party platforms. Instead, they gain a reusable asset that can be deployed across multiple departments, from customer service to legal, HR, marketing, and operations.

In fact, once developed, a custom LLM can be fine-tuned incrementally with minimal cost to support new functions or user roles—maximizing long-term ROI.

4. Strategic Control Over AI Capabilities

With public models, businesses are at the mercy of third-party update cycles, model versions, or feature restrictions. Custom development gives enterprises full strategic control over model architecture, fine-tuning frequency, interface design, and output formats.

This control is especially valuable for companies in regulated industries or those that require explainable AI outputs for auditing and compliance.

5. Cross-Functional Automation

Custom LLMs are versatile. A single base model can be adapted for multiple departments. For example:

In HR, it can summarize resumes and generate interview questions.

In finance, it can analyze contracts and detect discrepancies.

In legal, it can extract key clauses from long agreements.

In sales, it can draft client proposals in minutes.

In support, it can offer contextual responses from company knowledge bases.

This level of internal automation isn’t just cost-saving—it’s transformative.

Use Cases Driving Adoption

Let’s look at real-world scenarios where companies are seeing tangible benefits from investing in custom LLMs.

Legal and Compliance

Law firms and legal departments use LLMs trained on case law, internal documentation, and policy frameworks to assist with contract analysis, regulatory compliance checks, and legal research.

Custom models significantly cut down time spent on manual review and reduce the risk of overlooking key clauses or compliance issues.

Healthcare and Life Sciences

In healthcare, custom LLMs are trained on clinical data, EMRs, and research journals to support faster diagnostics, patient communication, and research summarization—all while ensuring HIPAA compliance.

Pharmaceutical companies use them to analyze medical literature and streamline drug discovery processes.

Finance and Insurance

Financial institutions use custom models to generate reports, review risk assessments, assist with fraud detection, and respond to customer queries—all with high accuracy and in a compliant manner.

These models can also be integrated with KYC/AML systems to flag unusual patterns and speed up customer onboarding.

Retail and eCommerce

Brands are leveraging custom LLMs for hyper-personalized marketing, automated product tagging, chatbot-driven sales assistance, and voice commerce. Models trained on brand data ensure messaging consistency and deeper customer engagement.

The Technology Stack Behind Custom LLM Development

Building a custom LLM involves a blend of cutting-edge tools and best practices:

Model selection: Companies can start from foundational models like LLaMA, Mistral, or Falcon, depending on their scale and licensing needs.

Fine-tuning: Using internal datasets, LLMs are fine-tuned to reflect domain-specific language, tone, and context.

Reinforcement learning: RLHF (Reinforcement Learning with Human Feedback) can be used to align model outputs with business goals.

Deployment: Models can be deployed on-premise, in private clouds, or with edge capabilities for compliance and performance.

Monitoring: Continuous evaluation of accuracy, latency, and output quality ensures optimal performance post-deployment.

Many companies partner with LLM development service providers who specialize in this full-stack development—from model selection and training to deployment and maintenance.

Why Now? The Timing Is Right

Several converging trends make 2025 the ideal time to invest in custom LLMs:

Open-source LLMs are more powerful and accessible than ever before.

GPU costs have declined, making training and inference more affordable.

Enterprise AI maturity has improved, with clearer internal processes and governance models in place.

Customer expectations are higher, and personalized, AI-driven experiences are becoming the norm.

New developer tools for fine-tuning, evaluating, and serving models have matured, reducing development friction.

Together, these factors have lowered the barrier to entry and increased the payoff for custom LLM initiatives.

Conclusion

As AI becomes more deeply integrated into the modern business stack, the limitations of one-size-fits-all models are becoming clear. Companies need smarter, safer, and more context-aware AI tools—ones that speak their language, understand their data, and respect their constraints.

Custom LLM development services offer exactly that. By investing in tailored models, organizations unlock the full potential of AI to automate tasks, reduce costs, and deliver exceptional customer and employee experiences.

For businesses looking to lead in the age of AI, the decision is no longer “if” but “how fast” they can build and deploy their own LLMs.

[starlinkflash]

GEO, LLMO, AEO… It’s All Just SEO

As a marketer, I want to know if there are specific things I should do to improve our LLM visibility that I am not currently doing as part of my routine marketing and SEO efforts.

So far, it doesn’t seem like it.

There seems to be massive overlap in SEO and GEO, such that it doesn’t seem useful to consider them distinct processes.

The things that contribute to good visibility in search engines also contribute to good visibility in LLMs. GEO seems to be a byproduct of SEO, something that doesn’t require dedicated or separate effort. If you want to increase your presence in LLM output, hire an SEO.

Sidenote.

GEO is “generative engine optimization”, LLMO is “large language model optimization”, AEO is “answer engine optimization”. Three names for the same idea.

How to improve LLM visibility

It’s worth unpacking this a bit. As far as my layperson’s understanding goes, there are three main ways you can improve your visibility in LLMs:

1. Increase your visibility in training data

Large language models are trained on vast datasets of text. The more prevalent your brand is within that data, and the more closely associated it seems to be with the topics you care about, the more visible you will be in LLM output for those given topics.

We can’t influence the data LLMs have already trained on, but we can create more content on our core topics for inclusion in future rounds of training, both on our website and third-party websites.

Creating well-structured content on relevant topics is one of the core tenets of SEO—as is encouraging other brands to reference you within their content. Verdict: just SEO.

2. Increase your visibility in data sources used for RAG and grounding

LLMs increasingly use external data sources to improve the regency and accuracy of their outputs. They can search the web, and use traditional search indexes from companies like Bing and Google.

OpenAI’s VP Engineering on Reddit confirming the use of the Bing index as part of ChatGPT Search.

It’s fair to say that being more visible in these data sources will likely increase visibility in the LLM responses. The process of becoming more visible in “traditional” search indexes is, you guessed it, SEO.

3. Abuse adversarial examples

LLMs are prone to manipulation, and it’s possible to trick these models into recommending you when they otherwise wouldn’t. These are damaging hacks that offer short-term benefit but will probably bite you in the long term.

This is—and I’m only half joking—just black hat SEO.

Why GEO is the same as SEO

To summarize these three points, the core mechanism for improving visibility in LLM output is: creating relevant content on topics your brand wants to be associated with, both on and off your website.

That’s SEO.

Now, this may not be true forever. Large language models are changing all the time, and there may be more divergence between search optimization and LLM optimization as time progresses.

But I suspect the opposite will happen. As search engines integrate more generative AI into the search experience, and LLMs continue using “traditional” search indexes for grounding their output, I think there is likely to be less divergence, and the boundaries between SEO and GEO will become even smaller, or nonexistent.

As long as “content” remains the primary medium for both LLMs and search engines, the core mechanisms of influence will likely remain the same. Or, as someone commented on one of my recent LinkedIn posts:

“There’s only so many ways you can shake a stick at aggregating a group of information, ranking it, and then disseminating your best approximation of what the best and most accurate result/info would be.”

Aedan Johnston, Senior Marketing Manager, Data, Monks

How GEO is (slightly) different from SEO

I shared the above opinion in a LinkedIn post and received some truly excellent responses.

Most people agreed with my sentiment, but others shared nuances between LLMs and search engines that are worth understanding—even if they don’t (in my opinion) warrant creating the new discipline of GEO:

1. Unlinked brand mentions matter more

This is probably the biggest, clearest difference between GEO and SEO. Unlinked mentions—text written about your brand on other websites—have very little impact on SEO, but a much bigger impact on GEO.

Search engines have many ways to determine the “authority” of a brand on a given topic, but backlinks are one of the most important. This was Google’s core insight: that links from relevant websites could function as a “vote” for the authority of the linked-to website (a.k.a. PageRank).

LLMs operate differently. They derive their understanding of a brand’s authority from words on the page, from the prevalence of particular words, the co-occurrence of different terms and topics, and the context in which those words are used. Unlinked content will further an LLM’s understanding of your brand in a way that won’t help a search engine.

As Gianluca Fiorelli writes in his excellent article:

“Brand mentions now matter not because they increase ‘authority’ directly but because they strengthen the position of the brand as an entity within the broader semantic network. When a brand is mentioned across multiple (trusted) sources: The entity embedding for the brand becomes stronger. The brand becomes more tightly connected to related entities. The cosine similarity between the brand and related concepts increases. The LLM ‘learn’ that this brand is relevant and authoritative within that topic space.”

Gianluca Fiorelli, Strategic and International SEO Consultant

Many companies already value off-site mentions, albeit with the caveat that those mentions should be linked (and dofollow). Now, I can imagine brands relaxing their definition of a “good” off-site mention, and being happier with unlinked mentions in platforms that pass little traditional search benefit.

As Eli Schwartz puts it,

“In this paradigm, links don’t need to be hyperlinked (LLMs read content) or restricted to traditional websites. Mentions in credible publications or discussions sparked on professional networks (hello, knowledge bases and forums) all enhance visibility within this framework.”

Eli Schwartz, Growth Advisor and Strategic SEO Consultant

Track brand mentions with Brand Radar

You can use our new tool, Brand Radar, to track your brand’s visibility in AI mentions, starting with AI Overviews.

Enter the topic you want to monitor, your brand (or your competitors’ brands), and see impressions, share of voice, and even specific AI outputs mentioning your brand:

2. Off-topic links and rankings matter less

I think the inverse of the above point is also true. Many companies today build backlinks on websites with little relevance to their brand, and publish content with no connection to their business, simply for the traffic it brings (what we now call site reputation abuse).

These tactics offer enough SEO benefit that many people still deem them worthwhile, but they will offer even less benefit for LLM visibility. Without any relevant context surrounding these links or articles, they will do nothing to further an LLM’s understanding of the brand or boost the likelihood of it appearing in outputs.

3. Different content types impact visibility

Some content types have relatively little impact on SEO visibility but greater impact on LLM visibility.

We ran research to explore the types of pages that are most likely to receive traffic from LLMs. We compared a sample of pageviews from LLMs and from non-LLM sources, and compared the distribution of those pageviews.

We found two big differences: LLMs show a “preference” for core website pages and documents, and a “dislike” for listing collections and listings.

Citation is more important for an LLM than a search engine. Search engines generally surface information alongside the source that created it. LLMs decouple the two, creating an extra need to prove the authenticity of whatever claim is being made.

From this data, it seems the majority of citations fall into the “core site pages” category: a website’s home page, pricing page, or about page. These are crucial parts of a website, but not always big contributors to search visibility. Their importance seems greater for LLMs.

A slide from my brightonSEO talk showing how AI and non-AI traffic is distributed across different page types.

Inversely, listings pages—think big breadcrumbed Rolodexes of products—that are created primarily for on-page navigation and search visibility received far fewer visits from LLMs. Even if these page types aren’t cited often, it’s possible that they might further an LLM’s understanding of a brand because of the co-occurrence of different product entities. But given that these pages are usually sparse in context, they may have little impact.

Lastly, website documents also seem more important for LLMs. Many websites treat PDFs and other forms of documents as second-class citizens, but for LLMs, they are a content source like any other, and they routinely cite them in their outputs.

Practically, I can imagine companies treating PDFs and other forgotten documents with more importance, on the understanding that they can influence LLM output in the same way any other site page would.

4. LLMs benefit from unique document structures

The point that LLMs can access website documents raises an interesting point. As Andrej Karpathy points out, there may be a growing benefit to writing documents that are structured first and foremost for LLMs, and left relatively inaccessible to people:

“It’s 2025 and most content is still written for humans instead of LLMs. 99.9% of attention is about to be LLM attention, not human attention. E.g. 99% of libraries still have docs that basically render to some pretty .html static pages assuming a human will click through them. In 2025 the docs should be a single your_project.md text file that is intended to go into the context window of an LLM. Repeat for everything.”

Andrej Karpathy 

This is an inversion of the SEO adage that we should write for humans, not robots: there may be a benefit to focusing our energy on making information accessible to robots, and relying on the LLMs to render the information into more accessible forms for users.

In this way, there are specific information structures that can help LLMs correctly understand the information we provide.

For example, Snowflake refers to the idea of “global document context”. (H/T to Victor Pan from HubSpot for sharing this article.)

LLMs work by breaking text into “chunks”; by adding extra information about the document throughout the text (like company name and filing date for financial text), it’s easier for the LLM to understand and correctly interpret each isolated chunk, “boosting QA accuracy from around 50%-60% to the 72%-75% range.”

Understanding how LLMs process text offers small ways for brands to improve the likelihood that LLMs will interpret their content correctly.

5. LLMs train on data that doesn’t impact SEO

LLMs also train on novel information sources that have traditionally fallen outside the remit of SEO. As Adam Noonan on X shared with me: “Public GitHub content is guaranteed to be trained on but has no impact on SEO.”

Coding is arguably the most successful use case for LLMs, and developers must make up a sizeable portion of total LLM users.

For some companies, especially those selling to developers, there may be a benefit to “optimizing” the content these developers are most likely to interact with—knowledgebases, public repos, and code samples—by including extra context about your brand or products.

6. LLMs don’t render JavaScript

Lastly, as Elie Berreby explains:

Elie Berreby, Senior SEO Strategist, Semkig.com

This is more of a footnote than a major difference, for the simple reason that I don’t think this will remain true for very long. This problem was solved by many non-AI web crawlers, and will be solved by AI web crawlers in short order.

But for now, if you rely heavily on JavaScript rendering, a good portion of your website’s content may be invisible to LLMs.

Final thoughts

But here’s the thing: managing indexing and crawling, structuring content in machine-legible ways, building off-page mentions… these all feel like the classic remit of SEO.

And these unique differences don’t seem to have manifested in radical differences between most brands’ search visibility and LLM visibility: generally speaking, brands that do well in one also do well in the other.

Even if GEO does eventually evolve to require new tactics, SEOs—people who spend their careers reconciling the needs of machines and real people—are the people best-placed to adopt them.

So for now, GEO, LLMO, AEO… it’s all just SEO.

Further reading

A guide to Semantics or how to be visible both in Search and LLMs.

LLM SEO best practices and hacks?

AI crawlers do not render JavaScript, so sign your texts!

SEO Is the Worst It’s Ever Been (And It’s Still Your Best Marketing Channel)

Article by

Ryan Law

Ryan Law is the Director of Content Marketing at Ahrefs. Ryan has 13 years experience as a writer, content strategist, team lead, marketing director, VP, CMO, and agency founder. He's helped dozens of companies improve their content marketing and SEO, including Google, Zapier, GoDaddy, Clearbit, and Algolia. He's also a novelist and the creator of two content marketing courses.

https://uk.linkedin.com/in/thinkingslow

[nekkuroi]

Daily reminder that if you have the money to pay for searching instead of being the product, Kagi is great! No SEO (search engine optimization) shit. Tells you if a website has a paywall, and lets you change priorities for domains. Miss having Wikipedia as a top result? set it to pinned. Want nothing to do with pinterest? block it.

It's $5 for 300 searches a month, or $10 for unlimited. Personally I literally do more than a thousand searches a month, so that is less than a cent per search. And I find the results are better than current Google.

As a disclaimer, they do have a couple of LLM (AI) products, but their focus is on summarizing documents and searches, with citations for each statement. They are not trying to replace creativity or dump content, just have actual productivity tools that relate to searching the web. They do care about keeping the internet human, as they state in their small web project.

[ioweb3tech]

Unlocking Innovation with Generative AI Development: The Future is Now

Artificial Intelligence is no longer just a support tool—it’s at the forefront of innovation. Among its many branches, Generative AI is rapidly changing how businesses create, operate, and scale. From generating content and designing visuals to building code and automating processes, Generative AI Development is setting new standards for digital transformation.

In this blog, we’ll explore what Generative AI is, how it works, and why your business should invest in it. Whether you're a startup looking to integrate smart features or an enterprise aiming to streamline workflows, this guide will show you the future powered by Generative AI.

What Is Generative AI?

Generative AI refers to algorithms that can generate text, images, code, music, and other types of content. Unlike traditional AI models, which classify or predict based on existing data, generative models create entirely new outputs based on learned patterns.

Popular tools like ChatGPT, DALL·E, and Midjourney are examples of generative models that use techniques like:

Transformer-based Neural Networks

Large Language Models (LLMs)

GANs (Generative Adversarial Networks)

Variational Autoencoders

These models learn from massive datasets and then produce high-quality, contextually accurate outputs that feel human-like and original.

Why Generative AI Development Matters for Your Business

The possibilities of generative AI go far beyond content generation. Businesses across industries—from healthcare and fintech to marketing and eCommerce—are tapping into generative capabilities to improve productivity, personalization, and user engagement.

Here’s how it can add value to your organization:

1. Content Creation at Scale

Whether it’s blogs, product descriptions, social media posts, or marketing emails, generative AI can automate content creation without compromising on quality or tone.

2. Product Design and Prototyping

Design mockups, wireframes, or even 3D models can be auto-generated, reducing development time and increasing creative flexibility.

3. Code Generation and Automation

Developers can use AI-assisted coding tools to generate boilerplate code, debug software, and even automate testing.

4. Personalized User Experiences

From AI-generated recommendations to custom landing pages, businesses can create hyper-personalized experiences for every user.

5. Business Intelligence & Decision Support

Generative AI models can summarize large datasets, extract insights, and provide intelligent suggestions for strategic decision-making.

Real-World Use Cases of Generative AI

Let’s look at how leading industries are already using generative AI:

E-Commerce: Auto-generating product titles and SEO-friendly descriptions.

Healthcare: Creating patient reports and medical summaries from data.

Gaming: Developing game characters, narratives, and world-building assets.

Marketing: Generating ad copies, creative visuals, and A/B testing ideas.

Finance: Generating financial reports and predictive models for analysis.

As you can see, Generative AI Development is not a trend—it’s a transformative tool.

The Role of a Generative AI Development Company

Implementing AI requires more than an idea. You need a strategic partner who understands both the technology and your business goals.

A professional Generative AI Development company can help you:

Define the best use cases for your business

Select the right model architecture (GPT, BERT, GANs, etc.)

Train custom AI models on proprietary datasets

Deploy AI solutions with real-time responsiveness and scalability

Ensure compliance, security, and ethical AI standards

With the right development team, you can move from experimentation to execution with confidence.

Why Choose Ioweb3 for Generative AI Development?

At Ioweb3, we specialize in building AI-powered products that deliver real-world value. Our team of AI engineers, product strategists, and data scientists work together to build, train, and deploy generative models tailored to your business.

Our Strengths Include:

💡 Deep expertise in NLP, LLMs, and AI infrastructure

⚙️ End-to-end services from ideation to deployment

🔒 Data privacy, security, and responsible AI compliance

🚀 Scalable, cloud-ready architecture

Whether you're looking to automate operations, create intelligent apps, or integrate with Web3 solutions, we’re here to bring your AI vision to life.

Key SEO Keywords to Watch

As you explore and plan your AI strategy, here are some relevant keywords you’ll come across:

Generative AI development

AI product development

SaaS experts

Web3 development company

Hire developers

These terms are shaping the future of tech—embedding them into your product roadmap and strategy is critical.

Final Thoughts

Generative AI is no longer experimental. It’s mature, powerful, and ready for real-world business impact. Companies that adopt it today are gaining a competitive edge by automating tasks, creating better content, and delivering smarter digital experiences.

If you’re ready to unlock this potential, it’s time to explore Generative AI Development with a trusted technology partner.

Let AI elevate your business to new heights—starting now.

[wachi-delectrico]

Ok, instead of just getting angry at these people (which will probably just reaffirm their usage of ChatGPT in these ways to themselves), I'll offer alternatives that don't rely on LLMs (Large Language Models) like ChatGPT. All a LLM does is write sentences, and they're not reliable to write factual information, even when you feed them the information yourself. See: the case of the dangerous edible mushroom identification book, the issues it has with processing large chunks of information for summary, the problems ChatGPT has as a search engine, and how incompetent ChatGPT is at solving math problems.

Alternative for cooking:

If you have food at home and can't come up with anything to cook, try MyFridgeFood or SuperCook, it shows you recipes based on what you already have laying around (SuperCook shows you both recipes that match your available ingredients exactly and recipes that may have additional ingredients, both of which you can filter). If you want to be more thorough, AnyList shows you recipes, saves your ingredients, and can be used to generate grocery shopping lists based on those, and can be used for long-term meal planning. PlanToEat lets you save recipes from anywhere on the web and can automatically generate shopping lists based on them. Besides those, I find it useful to go to the supermarket and walk around until I get a vague craving for what I want, then look up recipes and make a shopping list based on that.

Alternative for summarizing:

Recall is an AI-based solution specifically built for summarizing both files and web content. There are many others like it. If you want human-made pre-made summaries for things, check out SparkNotes and CliffsNotes. If you want to cross-reference any notes, use an academic search engine such as Web of Science.

Alternative for search engine:

It's obvious to any seasoned user that the quality of Google as a search engine has significantly decreased throughout the years, being absurdly vulnerable to SEO spam. The most popular Google Search alternatives are DuckDuckGo and Brave Search, as they more often than not offer good results while protecting user privacy. If you want the best of the best, and are willing to pay for the best of the best, a lot of people recommend Kagi. If you struggle to find what you're looking for regardless of browser, perhaps you should brush up on how to do web searches more efficiently (plus each search engine has its own keywords and search operators you can learn to improve your search results).

Alternative for maths:

if you're struggling to solve a maths problem yourself, you can input your problem into Photomath and it'll show you a step by step guide on how to solve it for free. Another alternative for this purpose is Symbolab, and though it shows you the step-by-step of each problem, you can only see the specifics of the explanation with a subscription. Personally, my favourite online maths solver with steps for when I'm stuck is Cymath, which shows solutions and steps for free, though it only comprehends some Algebra/Calculus problems thus far. If none of those help you truly understand a problem or a subject, you can ask at Mathematics Stack Exchange (no shame in asking!). If you don't have a scientific calculator, you can try the Desmos website, or the NumWorks app or website (a free emulated version of the actual physical graphing calculators they produce).

Just like all these, whatever it is you want done by AIs like ChatGPT, you can find other programs (and ever other AI-based programs!) made tailored to your specifications. By choosing the alternatives you protect yourself from misinformation.

[hisevenblog]

Be Seen, Be Cited: How Generative Engine Optimization Rules ChatGPT

Once upon a time, in a meeting room where all digital marketers sit around a big round table, with one simple question in mind, "How do I rank on Google?". All brainstorming on every SEO (search engine optimization) strategies, backlinking every where, using every effective keywords just to let Google notice. Such question is still asked but a new one emerged recently, which sound like "How do I get ChatGPT to quote me?". If this the question been asked then we're now getting into the new era of GEO which stands for generative engine optimization.

What's GEO? And What's The Different With SEO?

While traditional SEO is focused on guiding user to find their contents, built around of keywords, backlinks and metadata. Then to show up or get ranked by Google on SERPs, preferable 1st page. While for GEO, it can be known as the natural evolution of SEO. GEO is about helping AI to understand, trust and repeat the content even no user clicks on any links. So it's about optimizing human search behavior, but more into machine understanding:

Quoted or cited in AI-generated answers

Mentioned or referenced when people ask AI engines questions

Recognized as a credible source by large language models (LLMs)

Generative engines like ChatGPT, Copilot or Grok don't return the links as the way Google does. It summarize information and pass it back to users, based on most trusted sources.

GEO vs SEO: Key Differences

While SEO optimizes for discoverability, GEO optimizes for credibility in machine learning models.

Why GEO Matters Now?

Lot of users, including us. might straight asking ChatGPT for what dinner places tonight rather than going for Google. AI will summarize for us the answers and even together with the suggestions. Even the user never visits the site directly, it will comes out as suggestions, with goes as trusted choices even by AI. It can be said if your content are not being picked by AI, it also invisible to large segments of your audience. Being cited by AI boost your contents and organic reach like it was automatically promoted by AI.

Ways to Optimize for Generative Engines

With similarities with SEO (treat it as evolution), GEO can be optimized too. There goes some of the way:

Clarity and Structure - AI reading through some that clean and easy to read. With headers, subtitles and bullet points, it makes the entire contents easier to go through. Having a summaries even better.

Consistencies and Accuracies - Same as SEO, earning backlinks from reputable sites improving the authorities. Simply getting mentioned in news, articles, forums or reference sites help on this.

Semantic SEO - Rather than keywords, goes for full entities, like the places or people names. Even better if clearly explain it with descriptions.

Be Quoted on Popular Sites - AI usually goes for those indrustry blogs with proper attribution like Medium, Quora or LinkedIn articles.

Google Analytics or Search Console for GEO?

Unlike SEO, there's no proper way to track whether AI quoting your site or not. But if really want to then there's ways, even rather unconventional:

Ask ChatGPT of your industry related question, see if it ever prompt yours. If mentioned, then great success!

Check your sites if it ever crawled by AI bots.

If there's sudden spikes in direct visits, referral visits or branded search traffics (from GSC)

For now, there's haven't a conventional ways to track it yet. Over time, some AI tools may offer opt-in analytics or publisher insights. It could be additional features too that to be added on SEO software suites like Semrush or Ahrefs

It's Not a Replacement... It's EXPANSION!

As both having the same destination, to get noticed by their audience. Ranking on Google is still needed, but now you may also want to train the AI to see your content as trustworthy also. AI continues to influence how people gather information and some may replacing google as source of information. This become a sign that content strategies must adapt, so it's time to chat more about your contents with ChatGPT!

[smitharticle]

Understanding Google SGE: A Shift in Search Experience

The Google SGE (Search Generative Experience) is an AI component provided by Google that changes the way we as users now engage with SERPs (Search Engine Results Pages). With the advent and expansion of generative AI, Google is making this option available so users can explore search in a way that makes the information more interactive, conversational and informative than ever before. In many respects, this is profoundly changing the method at which we forager information and ultimately allows the user the option to highlight a lot context rich information that AI generates, right at the top of the SERPs. Google SGE is similar to standard search snippets in that they both represent an overview of the user's query, but SGE uses large language models (LLMs) or a fast, AI-based overview of the user's request. More specifically, Google SGE aggregates and organizes the case-related information from credible sources and presents it as a summary to the user. Consequently, the user not only saves time, but the overall search experience is enhanced by providing not only concrete information but providing concrete answers, instead of compelling the user to click through multiple links.

How Google SGE Works

When users enter in a search term, Google SGE instantly curates a snapshot — a block of AI-generated text — appearing above the normal search results. This AI Overview replicates insights, facts, and sometimes even questions to ask afterwards. Users can expand the section to dive deeper into links to the websites where the AI sourced its information.

For example, if you search “best digital marketing strategies in 2025”, instead of just a list of websites, SGE would return an AI generated summary of what top strategies are trending, pulling from reputable sources. The enhancements in relevance and efficiency are a big step forward for Google SERP features.

Why Google SGE Matters for SEO

The SGE Google update is changing the SEO game in a now very meaningful way. With the presence of AI-generated content occupying the SERPs and taking the top positions, traditional organic listings are pushed down further in the results. This could lead to drops in clicks for sites that are hyper dependent on top organic positions. 

As a result, the SEO professional community needs to adopt content optimization for AI Overviews, generative searches, whatever it is called. Content must be clear and concise, with headings and content clear and simple. The AI recognizes content that is authoritative, simple, and answers the users query. They'll likely embrace content that matches those three criteria, along with focusing on intent, which includes the keyword intent.

Moreover, websites that demonstrate strong E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) are more likely to be cited by Google's generative AI. Therefore, enhancing content quality and building backlinks from trusted sources are key strategies to maintain visibility.

Future of Search with AI Integration

Google SGE is in Search Labs and rolling out slowly to more users, representing Google's long-term vision for a more conversational, intelligent search experience. This update is not only important for users as it simplifies complex search behavior, but it also challenges both marketers and businesses to re-imagine their approach to SEO.

In the future expect SGE to become even more personalized, using user behavior and preferences to deliver customized results. Expect voice search, mobile-first indexing, and real-time content to play an even larger role in the evolution of Google AI in search.

Conclusion

Google SGE is not simply a feature, it's a change in how search engines provide value. By using AI in a way that can deliver instant value for its users, it changes the expectations of what will come with the search experience. For content creators and SEO professionals, the expectation to adjust content strategies to this Search Generative Experience is critical. To stay in front of the changes, try to create the highest quality and most helpful content that resolves user intent and adheres to Google's continually changing process.