GEO, AEO and LLMO: scope comes before the acronym
New names for AI optimization help organize the conversation when they come with a scope. Three questions to assess any recommendation: which system, which information and which signal to track.
GEO, AEO, LLMO. Over the past few months, several names have emerged to describe the work of making content visible in AI-generated answers. My impression is that, in many cases, we are overcomplicating a relatively simple part of the problem.
I mean the start of the diagnosis, not how the systems work. AI-powered search engines and conversational assistants are complex, work differently from one another and change often. That is exactly why the first step needs to be clear.
What each acronym usually refers to
These terms circulate with definitions that vary from author to author. Broadly speaking, GEO (generative engine optimization) refers to presence in the answers of generative engines, AEO (answer engine optimization) to content structured to answer questions directly, and LLMO (large language model optimization) to how language models represent a brand or a subject. There is considerable overlap between them, and none has an official definition shared by the systems themselves.
That does not make the terms useless. They can help organize a conversation, as long as they come with a scope.
What Google says, and where it applies
In Google’s case, the guidance is explicit. The Search Central guide on generative features keeps SEO as the foundation, states that structured data is not required for generative search and that there is no special schema.org markup to add (Google Search Central, AI optimization guide). The same guide says there is no need to create new machine-readable files, AI-specific text files or Markdown versions to appear in Google Search.
The page on AI Overviews and AI Mode reinforces that there are no additional requirements or special optimizations for these formats beyond the best practices already known (Google Search Central, AI features and your website).
This guidance applies to Google. It does not justify treating every assistant as if it were the same product. Other systems may use their own indexes, content partnerships, real-time search or only what the model learned during training, and each of these paths calls for a different kind of observation.
Three questions before any recommendation
When I come across a new “AI optimization” recommendation, I try to answer three questions before taking it on board.
Which system? Does the recommendation apply to Google, to a specific assistant or to all of them? If the source does not say, the recommendation is probably generalizing more than it should.
Which information? Is the goal for the system to find a page, describe a product correctly, cite a source with a link or recommend a brand in a comparison? These are different problems, with different causes.
Which signal will we track? How could we check whether the change had an effect, and how long would that take?
| goal | what to observe |
|---|---|
| mention | the brand appears in the answer, with or without a link |
| linked citation | the page is listed as a source |
| visit | there are clicks and sessions coming from that surface |
| accurate description | the answer represents the product without errors |
“AI visibility” can mean any of these rows, and each one requires its own way of tracking. I went into this distinction in more detail in Found, cited, visited.
The problem described before the proposal
In practice, this care changes the conversation with anyone requesting or offering optimization work. Before discussing which acronym to put in the proposal, I prefer to see the problem clearly described: in which system the content does not appear, which information is missing or wrong, and which signal will indicate improvement.
When those answers exist, the acronym matters little. When they do not, no acronym makes up for the missing diagnosis. And, at least in Google’s case, the starting point is still the same work as always: accessible pages, useful content and clear information.
References
Keep reading
Read next
Found, cited, visited: how to separate the outcomes of AI search
SEO is still the foundation, but a page can now appear as a source for a generated answer. Four questions to track this second competition without confusing a mention with a result.
Stop asking AI to improve your writing
Asking for improvement and asking for an audit are different tasks. The workflow I use to find problems without outsourcing the editorial decision.
How to delegate execution to AI without delegating the decision
AI can research, organize and propose versions. What needs to be clear before the request is who decides what is worth doing and by which criterion the result will be judged.