1. Introduction — the moment when things stop working
You publish content that you find solid. You've done the work properly: structure, SEO, angles, internal linking. Nothing to complain about. You test.
On Google, it holds up pretty well. On Perplexity, surprise, you're cited. And on ChatGPT… nothing. Not a trace. As if your content didn't exist.
You adjust. You rephrase. You add more material. You re-test. Still nothing.
That's exactly the moment when you realize something is wrong, but not where you thought.
The problem isn't your content itself.
The problem is that you're applying a single logic to LLMs that absolutely don't work the same way.
And there, you need to be honest: continuing to produce content "optimized" without distinguishing between LLMs is accepting to have random results. Sometimes it works, often it doesn't, and above all you never really know why.
GEO isn't a layer on top of SEO. It's a change in logic. And this change starts with a simple idea:
→ All AI engines don't play by the same rules
→ And if you don't adapt, you mechanically lose visibility
2. Why each LLM works differently
We tend to lump everything together: ChatGPT, Perplexity, Google AI… as if they were just variations of the same system. In reality, these are three very different approaches to information.
◼ ChatGPT works primarily through synthesis. It doesn't search for pages, it reconstructs answers from what it "knows" or can exploit. It rephrases, condenses, simplifies. Sources, when they exist, often take a back seat.
◼ Perplexity, conversely, works like an augmented search engine. It searches for pages, selects them, then builds an answer by explicitly citing its sources. You can see in black and white where the information comes from.
◼ Google AI Overviews is in between. It relies on its SEO index, adds a layer of understanding, then generates an answer directly in the SERP. It's hybrid, and sometimes downright disorienting.
What's interesting is that these differences aren't theoretical. They have a direct impact on your visibility.
A piece of content can be:
→ invisible on ChatGPT
→ very visible on Perplexity
→ partially picked up on Google
And it's not an anomaly. It's normal.
Once you understand that, you change how you work. You're not producing "good content" anymore, you're producing content that needs to be interpreted differently depending on the LLM.
3. ChatGPT: logic, limits and opportunities
ChatGPT is probably the most frustrating LLM when you're doing GEO, because it gives you almost no direct feedback. You don't know why you're being used… or why you're being ignored.
But with some perspective, certain patterns emerge.
What influences your presence isn't your SEO in the classical sense. It's your ability to produce content that can be reused in an answer.
In other words:
→ clear ideas
→ logical structure
→ understandable blocks independently
Content that's too vague, too narrative, or too "SEO optimized" in the wrong way becomes unusable. It doesn't provide exploitable material.
And that's where many get it wrong.
They think that because content ranks on Google, it will be picked up by ChatGPT. In practice, it's often false.
Let's take a concrete example.
You have two pages on the same topic.
The first is well-optimized for SEO, with long sentences, context, filler, smooth transitions. It's pleasant to read, but fairly diffuse.
The second is more direct. It gets straight to the point, with clear sections, well-separated ideas, almost "modular".
In most cases, ChatGPT will rely on the second, even if it performs worse in SEO.
Why? Because it's exploitable.
And that's where the opportunity lies.
If you keep writing only for SEO, you miss out.
If you write to be understood, structured and reused, you start to exist.
It's a subtle change, but a profound one.
To dive deeper on this topic, you can explore the dedicated page on ChatGPT, because it's clearly the most counter-intuitive LLM.
4. Perplexity: the most exploitable LLM today
Perplexity is almost the opposite of ChatGPT. Where ChatGPT is opaque, Perplexity is readable. And honestly, today, it's the one that best allows you to progress quickly in GEO.
Why? Because it shows you its sources.
You know exactly which pages are used, when, and for which part of the answer. It's a level of transparency we've never had before.
Concretely, Perplexity does three simple things:
→ it searches
→ it selects
→ it synthesizes
And it cites.
This last part changes everything.
Because you can analyze what works.
You see a page is cited? You look at why.
You're not? You compare with those that are.
And very quickly, patterns emerge.
Content that works is almost always:
→ clearly structured
→ answer-oriented
→ without unnecessary detours
It's not necessarily "beautiful" content. It's effective content.
A simple example.
You write a page on "how to choose a surveillance camera".
Version 1: you tell the context, the stakes, you introduce gradually.
Version 2: you answer directly with criteria, use cases, concrete choices.
Perplexity will favor version 2, almost systematically.
And there, you have to be honest.
Today, if you want an LLM to understand GEO and test quickly, it's Perplexity.
It's the one that gives you the most feedback, the fastest.
You can explore this further on the dedicated page on Perplexity, because there's really a lever to exploit here.
5. Google AI Overviews: the evolution of SEO (and its pitfalls)
Google isn't just changing its interface. It's modifying the very mechanism of visibility.
Before, the logic was simple:
you were well positioned → you got traffic
Today, it's much more ambiguous.
You can be visible in an AI answer… without generating clicks.
And that's probably the most underestimated point right now.
Because many continue to analyze their performance with classic SEO metrics, while user behavior changes.
Let's take a real case.
You're positioned in the top 3 on a strategic query.
Google displays an AI Overview.
The user reads the answer directly in the SERP. They have their answer. They don't click.
Result:
→ your ranking hasn't moved
→ your traffic drops
And if you don't understand what's happening, you can quickly make bad decisions.
What you need to integrate is that Google remains Google. The fundamentals still matter:
→ authority
→ quality
→ structure
→ reliability
But that's no longer enough.
You must also think:
→ how your content can be picked up
→ which parts are extractable
→ how you appear in a synthesis
SEO doesn't disappear. It becomes incomplete.
If you want to go further on these mechanisms, the page on Google AI Overviews details the impacts and strategies to adapt well.
6. Claude and Copilot: different logics, but worth watching
Claude: depth and understanding
Claude isn't a search engine in the classical sense, and that's precisely what makes it interesting.
Where other LLMs favor quick answers or excerpts, Claude is capable of digesting longer, more argued content, with real overall coherence.
Concretely, that changes things.
Content that is:
- detailed
- well-structured
- with clear reasoning
has a better chance of being understood and reused.
Where Perplexity rewards the direct answer, Claude values the quality of reasoning.
It's a less "visible" LLM today in terms of pure SEO/GEO, but clearly underexploited.
If you produce expert content, it's a territory not to neglect. Head over to this special page on Claude to learn more.
Copilot: an extension of SEO via Bing
Copilot is harder to grasp, because it's not used like a classical LLM.
It's integrated into:
- Windows
- Edge
- Microsoft tools
So usage is often indirect.
But behind it, the logic is fairly clear:
Copilot relies heavily on Bing
Which means:
- a classical SEO foundation
- an AI layer on top
- little transparency on sources
The problem is you get very little feedback.
You can be used without seeing it.
Or not be used without understanding why. More details on Copilot.
What you need to remember
Claude and Copilot aren't priorities today if you're starting out in GEO.
But ignoring them completely would be a mistake.
Claude → interesting for expert and structured content
Copilot → interesting via a Bing SEO + AI logic
7. LLM comparison — a strategic reading
When you lay it all out, you realize these LLMs aren't playing the same game.
ChatGPT is a synthesis LLM. It doesn't give you direct visibility, but it influences overall perception. It's slow, diffuse, but powerful over the long term.
Perplexity is a citation LLM. It gives you immediate, measurable, actionable feedback. It's the ideal terrain for testing and optimizing.
Google AI is a hybrid engine. It has the broadest impact, but also the hardest to measure. You can be visible without getting traffic from it.
And that's where you need to make a strategic choice.
Not all LLMs deserve the same effort.
If you want to be effective:
→ Perplexity is the best entry point
→ Google AI is essential, but complex
→ ChatGPT is a long-term investment
If you want a more detailed view, the LLM Comparison lets you dig deeper into these differences with more concrete cases.
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8. Practical tips (what you can do right now)
Let's stay concrete.
First thing: test.
Not once, not quickly, but really.
Take a few important queries for your business and look at:
→ where you appear
→ how you're used
→ where you're absent
Then, analyze.
When you're cited, it's never random. There's a reason. A structure, a sentence, a section that was judged useful.
When you're not, it's even more interesting. It means your content isn't exploitable in this context.
From there, adapt.
Stop producing "global" pages.
Work on blocks. Answers. Autonomous sections.
You should be able to extract part of your content and have it make sense on its own.
And above all, avoid a classic mistake: duplicating your strategy.
What works on Perplexity doesn't necessarily work on ChatGPT.
What works on Google isn't enough to be picked up in an AI answer.
GEO is iteration.
You test, you adjust, you re-test. And you start again.
9. Conclusion — what you really need to remember
If you look at the whole picture, there's no single logic that dominates.
ChatGPT, Perplexity and Google AI don't read your content the same way, don't select the same elements, and especially don't create the same type of visibility. Trying to apply an identical method everywhere is mostly complicating things for a pretty uncertain result.
What works best today is a simpler and more lucid approach: observe how each LLM reacts, understand what it really values, then adjust your content accordingly. No need to reinvent everything, but clearly, sticking to a single logic no longer works.
With some perspective, GEO looks less like a "new discipline" than an adaptation of SEO to several parallel environments. And that's exactly what makes it interesting: there's still room to test, understand, and get ahead without necessarily multiplying resources.