Do you feel like the SEO you know has lost its bearings? That’s normal. The search engine as we’ve practiced it for twenty years is shifting, and the thunderous arrival of Generative Engine Optimization (GEO) is shaking every certainty. The answers you get via ChatGPT, Perplexity, or Claude no longer resemble a “results page,” but rather a direct answer where some brands appear… and others disappear. So, how can you ensure your content is properly cited by these models?

The real question isn’t just “how to be seen,” but “how to be retained by the model itself.” Because an LLM doesn’t just list sources—it rephrases, selects, and appropriates the information. We’re entering a logic where visibility is no longer about simple ranking, but about being present in an AI’s memory. And that changes everything, even if some principles inherited from SEO still apply.

GEO and the Shift from SEO to AI

SEO has long relied on keywords, backlinks, and technical audits. GEO, on the other hand, relies on the ability of generative models to extract and rephrase well-structured content. Since queries are now much longer (23 words on average) and conversational, generative engines favor texts that can be easily summarized. Concretely, an airy paragraph, clear lists, hierarchical headings, and concise sentences make all the difference.

In practice, this means that “keyword stuffing” strategies are now completely useless. AI looks for semantic density and coherence. A page that summarizes a topic with phrases like “in short” or “here are the key points” is much more likely to be picked up. Tests conducted by marketers show that bullet lists significantly increase citation occurrences in generative responses.

Finally, GEO fits into an ecosystem where the business model is changing. Traditional engines lived off advertising. LLMs, on the other hand, rely on subscriptions. Result: less incentive to massively send out traffic. Content is only cited if it truly enriches the answer given to the user. This is a shift that forces a rethink of visibility strategy.

Similarities and Differences Between GEO and SEO

It would be wrong to say that everything you’ve learned in SEO is obsolete. In reality, GEO shares several common points with SEO. Producing useful content, answering search intent, taking care of E-E-A-T (experience, expertise, authority, trustworthiness)… all of this remains a foundation. In fact, a site that already has good SEO rankings is very likely to be visible in generative engines.

The nuance lies in the output of the engines. An SEO result is a list of links. A GEO result is an answer written by AI, possibly with a few citations. The user therefore has less reason to click, unless they want to verify a source or dig deeper. Several analysts note that click-through rates are dropping, while conversion rates can actually be better, since the user already has a relationship of trust with the answer provided by the AI.

Another point: GEO is not uniform. Each engine has its own implicit rules. Perplexity cites more sources, Google AI Search sometimes integrates local maps, ChatGPT relies on different corpora. So it’s impossible to apply a single “miracle recipe.” The only certainty is that clarity, structure, and content authority remain the best weapons.

How to Measure and Manage GEO Visibility

The big current headache is measurement. With SEO, we had tools like Ahrefs or Semrush to track keyword rankings. With GEO, you need to monitor references in AI responses. New platforms like Profound or Goodie already offer dashboards capable of analyzing a brand’s presence in generative outputs.

A striking example comes from Canada Goose. The company used a GEO tool to understand how its name appeared in LLM responses. The result wasn’t just linked to products, but to the model’s ability to spontaneously mention the brand. This is a new form of notoriety: being integrated into the AI’s memory, without the user explicitly asking for it.

Some traditional tools are also responding. Ahrefs has launched “Brand Radar” to track mentions in Google’s AI Overviews. Semrush has developed a GEO-dedicated toolbox. These signals show that the profession is adapting and that GEO is becoming a discipline in its own right, with its own specific metrics.

Best Practices for Optimizing Content for GEO

The first rule is simple: structure. LLMs love content that’s easy to parse. Short paragraphs, lists, clear headings… anything that allows for quick extraction carries enormous weight. A marketer posted on LinkedIn that adding numbered lists multiplied their citations in ChatGPT responses.

Next, keep in mind that AI needs credible, sourced content. Integrating citations, figures, expert opinions… is a strong signal for the model to consider your content reliable. E-E-A-T, already important for Google, is even more so here.

Finally, think “natural language.” Users query AIs with long, almost conversational sentences. Incorporating this type of phrasing into your content helps the AI associate your pages with real queries. Tools like HubSpot AI Search Grader can also tell you if your content is “scan-friendly” for LLMs.

GEO and the Future of Content Marketing

Let’s be clear: GEO is not a gimmick. Major platforms are investing massively to integrate generative engines into every interface. Safari, for example, will include Perplexity and Claude. Google no longer has a monopoly.

The risk is reliving the chaos of SEO’s early years, when every algorithm update disrupted rankings. Here, every model update can change how a brand is cited. Marketing teams must therefore stay constantly alert, test, adjust, and measure.

At the same time, GEO opens up an unprecedented opportunity: influencing the models’ memory. Whoever manages to embed their brand in AIs’ standard responses gains unparalleled visibility. This may well be where the next great battle of digital marketing will be fought.