Language models like ChatGPT, Gemini, or Claude no longer just read your pages. They synthesize them, summarize them, and decide if they are worth quoting in an answer. At the same time, Google is starting to deploy a new interface: The AI Overview (AI Overview), which displays an AI-generated response at the top of the results, often without a click.
This evolution is profoundly transforming the logic of natural referencing. Now, what matters is not only the position in the SERP, but the ability of your content to be understood, reformulated and reused by an AI.
That is the objective of the GEO (Generative Engine Optimization) : adapt your content to exist in AI environments, while respecting the basics of classic SEO. Here is a clear and concrete method to achieve this.
The development of generative AIs is no longer a matter of the future. In 2025, the global market for language models (LLM) will reach 7.13 billion Canadian dollars, on the rise of 28.3% over one year (sources: The Business Research Company).
AIs don't “read” like a human
Content may be well positioned on Google but invisible to an AI. Why? Because models like GPT-4 or Gemini analyze information in 3 successive layers:
- Is the content accessible?
→ Can the AI load the page, read the HTML, ignore the blocking JavaScript? - Is the content understandable?
→ Is it well-structured, clear, segmented, with an explicit answer at the beginning of the section? - Is the content reusable?
→ Is it reliable, updated, sourced, and reformulable without ambiguity?
In other words: to be taken up by an LLM, it is not enough to write a good article. You have to produce content technically readable, semantically structured and cognitively usable.
Step 1: ensure the technical accessibility of your content
Before even optimizing content for artificial intelligence, it still has to be visible and legible by the systems that power LLMs and AI engines like Gemini. This step is often underestimated, despite the fact that it determines everything that follows.
LLMs rely largely on data collected by traditional crawlers (Googlebot, Common Crawl, etc.) to structure their understanding of the web. If your content is poorly rendered, too slow, or masked by an uninterpretable JavaScript layer, It will not be indexed, taken over, or cited.
It is The basic principle of GEO : Optimization starts with accessibility.

Make your pages readable without JavaScript
Sites designed in React, Vue, or Angular (when they are not properly configured) rely on a logic of client-side rendering (CSR): it is the user's browser that executes JavaScript and displays the page. However, the majority of AI bots, including those used to train LLMs or generate AI Insights, do not load JavaScript or very partially.
As a result, your main content may simply be missing from the index.
Concrete solutions:
- Switch to server side rendering (SSR) : it is the browser that receives a complete HTML version, already ready to be read. Next.js or Nuxt.js allow it natively.
- Use a Generative CMS like Webflow : all content is generated in native HTML/CSS, immediately readable by bots.
- If you stay in SPA (Single Page Application) : implement pre-rendering (prerender.io, Rendertron...) for critical pages.
To test if your pages are readable:
- Use the command
Curl https://www.votresite.com/page
and check if the main text appears. - Inspect your pages in Google Search Console > URL Inspection > Googlebot Display.
- Move your site to Screaming Frog SEO Spider by deactivating JavaScript.
✅ If your main content is not displayed without JS, it will not be exploited by generative AI.
Optimize the speed and cleanliness of the rendering
Another point that is often overlooked: AIs don't wait. Too long a load time, too complex code, or a disorganized HTML structure can hinder or interrupt the analysis of your page.
This is all the more critical because Google, in its AI Insights, Only retains content that is rendered quickly, clean, and well-formatted. Gemini prioritizes technical performance as well as semantic relevance.
Technical objectives to aim for:
- LCP (Largest Contentful Paint) < 2s
- TTFB (Time To First Byte) < 500ms
- CLS (Cumulative Layout Shift) close to 0
Key optimizations:
- WebP or AVIF images, with defined dimensions and native lazy loading (
loading="lazy”
) - Brotli or Gzip compression enabled server side
- Semantic and hierarchical HTML, without unnecessarily nested tags (
<section><div><div><p>...
is to be avoided)
Clean up your HTML to improve interpretation
AIs better understand well-marked pages, where each section has a clear role. Disorganized HTML makes it difficult to analyze content, especially in the context of AI Insights, which need to extract a quick, structured, and reliable response.
Step 2: structure your content so that it can be picked up by an AI
Once your content is accessible, it must be organized so that it can be understood, extracted, and potentially reformulated by language models. Unlike a human reader who scans a page visually, an LLM isolates semantic blocks in order to interpret them. It cuts, rewrites, synthesizes.
In other words: good GEO content is not only readable. He is reformulable.
Start by answering the question
Generative models seek quick and explicit answers. When a user interacts with an LLM or triggers an AI Overview in Google, the aim of the algorithm is to provide a clear, synthetic and, if possible, immediate response.
This assumes that your content is starting by the answer, not by the introduction.
Example:
❌ “SEO has undergone many changes over the years...”
✅ “Generative Engine Optimization (GEO) consists in structuring content so that it can be understood and cited by an AI like ChatGPT or Gemini.”
This so-called “inverted funnel” structure (from the most useful to the most developed) allows the AI to directly extract the essentials while maintaining the possibility of developing the details downstream.
Use a logical hierarchy of titles
AIs rely heavily on title tags (<h1>
, <h2>
, <h3>
) to understand the organization of content.
Best practices:
- A single H1 per page, containing the main subject in interrogative or descriptive form
- Of H2 for each big idea (one per section)
- Of H3 to detail an argument, present a method, or structure a sub-idea
This creates a clear mind map that the AI can follow to detect key passages that need to be reformulated.

Format your paragraphs for semantic readability
AIs don't read style, they read structure. A paragraph that is too long, an idea drowned in verbiage, or a vague formulation hinder extraction.
Editorial objectives:
- One paragraph = an idea
- Maximum 3 to 5 sentences per block
- A style declarative and informative, without suspense or unnecessary literary effect
Avoid:
- Technical metaphors
- The long, unmarked digressions
- The ambiguous alternation between “you”, “us”, “we”, “we” (prefer a coherent voice)
Integrate formats extracted automatically by LLMs
Some content structures are better exploited by AIs because they facilitate reformulation or synthesis. It is essential to integrate them naturally into your articles.
These formats have a double benefit: they improve the reading experience for the user and increase the probability of inclusion in the responses generated.
Write rephrasable sentences “as is”
LLM-ready content is not based solely on its content. It should also contain segments Copyable and pastable directly in the response of an AI assistant.
✅ Example of a sentence that can be used by an AI:
“Google AI Insights are triggered for over 13% of queries in the United States in early 2025.”
❌ Useless sentence for an LLM:
“This point will be shone in more detail later in the article.”
In practice, this means regularly integrating:
- Of factual phrases
- Of standalone definitions
- Of partial summaries of a section within the section itself
Step 3: provide unique and verifiable added value
In a web saturated with similar content, LLMs seek to sort things out. Their aim is not only to summarize a well-structured page but to identify the sources that really contribute something new or reliable.
Content that simply repeats what we already find elsewhere, even well presented, is very unlikely to be taken up by GPT chat, Gemini, or in an AI Preview.
On the other hand, original information, specific statistics or precise feedback can become anchors for a generated response. This is where editorial strategy comes into its own.
Produce content that can only come from you
Language models favor content that demonstrates a real expertise because they are the ones who enrich their ability to respond.
This does not mean producing “complex” content, but content rooted in the reality of your business, which AI won't be able to find elsewhere.
Concrete examples:
- An analysis resulting from an A/B test on your own site
- Detailed feedback on a Webflow redesign
- Internal statistics (conversion rate, SEO, traffic...) that you can contextualize
- A methodology that you apply in an agency and that differs from the norm
These elements have two benefits:
- They differentiate you in visible web content.
- They position you as legitimate source in the responses generated.
Support your claims with reliable sources
AIs work as trusted mediators: they need to know only what they reuse is based on verifiable data.
So you need to do what few SEO writers still do today: cite your sources correctly.
Best practices to follow:
- Mention the name of the source (organization, study, publication)
- Add the date of publication if known
- Provide a hyperlink where relevant
- Cite industry reports, public data, academic work, or the results of internal experiences
✅ Example of a well-written quote:
“At the beginning of 2025, AI Insights were displayed for around 13% of queries in the United States based on the latest available data.”
❌ To avoid:
“Numerous studies show that...” or “According to some sources...”
This type of vague wording is ignored or even “sanctioned” by the most recent models, which emphasize precision and editorial responsibility. In 2024, 72% of the 25 most popular queries on Google included a brand explicitly mentioned by the user (sources: Search Engine Land). A sign that Internet users are looking for identified and contextualized content and that AIs tend to favor names that are clear, well-structured and easily cited.
Clearly identify the author and his legitimacy
Models like Gemini or Claude don't just assess the raw content: they also analyze the editorial context, including the perceived credibility of the author.
On an LLM-First optimized page, the author should not be an anonymous field in the metadata. He must appear clearly, with associated authority elements.
This reinforces the E-E-A-T (Experience, Expertise, Expertise, Authoritativeness, Trust) signal, which has become central to the Google algorithm and to the scoring of generative AIs.
Clearly show when it was published or updated
Language models are giving increasing importance to freshness of information. Not only because they incorporate recent corpora (in particular Gemini, Claude, GPT-4 Turbo), but also because they seek to avoid relaying obsolete data.
Content that is explicitly updated will be considered more reliable than content that has remained static for two years, even if it is still valid.
What you need to do:
- Indicate the date of updating (and not only for publication)
- Add a box or mention at the beginning or end of the article:
“Article updated on July 14, 2025"
- Avoid dynamic or misleading dates (e.g., “Published today”)
You can also schedule editorial updates every 6 to 12 months to stay on the AI radar.
Step 4: Optimize your content for Google AI Insights
The AI Overview (AI Overview) changes the way the results are displayed: the user gets a summary generated by AI (via Gemini) who answers his question directly, without necessarily clicking.
According to BrightEdge, as of May 2025, more than 11% of requests now trigger an AI Preview, which is an increase of 22% in one year (sources: BrightEdge). A clear signal: these displays are being deployed massively... and remain shorter, more accurate.

Understand how AI Preview works
Google builds this response in four steps:
- Intent detection : what the user is really looking for.
- Source selection : only those perceived as relevant and recent.
- Synthesis by Gemini : generation of an articulated summary.
- Partial attribution : some pages are cited in the sources.
A page positioned in 8th or 10th place can be included if it is structured, clear and verifiable.
Key criteria for inclusion
Concrete recommendations to apply now
- “Quick response” box (2-3 sentences) at the top of the page.
- Schema.org Tags (FAQPage, Article, HowTo) activated and tested.
- Content review at least twice a year, with visible updates.
- Reindexing via Search Console after each key update.
- Optimizing readability : precise titles, removal of digressions, short paragraphs.
If you are in local SEO or B2B, Also take care of your Google Business Profile, your opinions and your location-based data, AIs frequently draw on the local answers in the AI Overview.
Conclusion
According to Grand View Research forecasts, the global LLM market is expected to grow by 36% by 2030 with an expected acceleration in the health, finance and B2B content sectors (sources: Grand View Research). SEO is no longer just about position. With the rise of LLMs like ChatGPT, Gemini or Claude and the massive integration of AI Insights into Google, the ability to be understood, reformulated, and cited by an AI is becoming a determining criterion of visibility.
This requires a change in posture: writing to be found is no longer enough, you have to write to be. utilized by AI. This is what Generative Engine Optimization (GEO) is all about.
We incorporate this new requirement from the design stage:
- Of Webflow sites that are technically readable by crawlers and AI models
- One content architecture designed for extraction, the reformulation and the quotation
- Augmented SEO strategies, adapted to LLM challenges and the mutation of the SERP
Do you need expert support to adapt your SEO strategy to AI? Contact us here →

FAQ — SEO, LLM, and AI Overview
What is an LLM and why does it impact SEO?
An LLM (Large Language Model) is an artificial intelligence capable of understanding and generating natural language. It no longer simply indexes the web like a traditional search engine: it reformulates content to produce complete answers. This changes the SEO logic: You now have to write so that AIs can extract and reuse your content.
What is the difference between GEO and traditional SEO?
Traditional SEO aims to position yourself well in the SERP. The GEO (Generative Engine Optimization) aims to appear in a AI-generated response. The first works on indexing and ranking, the second on comprehension, rephrasing, and quoting by an LLM.
How do I know if my content is being cited by an AI like ChatGPT?
It is still difficult to track it down precisely, but there are some signs that can alert you:
- Your exact formulations reappear in the answers from ChatGPT or Gemini
- Your domain name is mentioned in Google AI Insights
- Traffic from platforms like Perplexity.ai or SearchGPT is increasing in your analytics
Do all pages on my site need to be optimized for AI?
No, but pages with a high level of informational importance (articles, guides, sectoral pages, resources) must be designed to be reformulated and extracted. Transactional or legal pages remain outside of this logic.
Should specific structured data be used for AI Insights?
Yes. The beacons FAQ page
, Article
, HowTo
, and Review
are particularly useful. They facilitate analysis by AI models, especially in cases of automatic reformulation or partial citation by Gemini. These tags can be tested and validated via the tool Google Rich Results Test.