What's the difference between AI search optimization, GEO, AEO, and LLMO? How we use the terms
A practitioner's guide to AI search optimization, GEO, AEO and LLMO: what each term means, why clients get confused, and how we turn all four into one measured program.
Clients send us job titles and RFPs that use all four terms interchangeably, sometimes in the same paragraph. We get asked which one is "right". Our answer is that they are different angles on one program, and the label matters less than what you measure. Still, it helps to know what people mean when they use each one.
How we hear the terms used
AI search optimization is the umbrella. When a marketing director says it, they mean "make sure we show up when buyers ask AI". It is a goal, not a method.
GEO (generative engine optimization) is the version we use most. It is about being mentioned and cited in the written answers that generative engines produce. It comes with a measurement vocabulary: visibility, share of voice, sentiment, citations, position in the answer.
AEO (answer engine optimization) comes from the older world of featured snippets and voice answers. When someone says AEO, they usually mean content design: give a clear answer to a specific question, early on the page, in a form that is easy to lift.
LLMO (large language model optimization) tilts toward how a model itself represents a brand. Think consistent facts, a clear identity, and good coverage on sites the model has learned from. Some teams use it as a synonym for GEO.
None of these have a formal standard. Vendors and agencies coin and reuse them differently. So when a client uses one, we ask what outcome they want and write it down.
Why the distinctions matter less than they sound
Almost every action helps all four. Rewriting a page so the answer comes first helps AEO, GEO and LLMO. Fixing crawler access helps all of them. Earning a mention on a review site or in a forum thread that models often cite helps all of them. We have never built a program where the acronym changed the task list.
What does change is who you are talking to. A content team responds to AEO because it sounds like editing. A search team understands GEO because it extends SEO. An executive likes "AI search optimization" because it needs no explanation. We use whichever word gets the work approved.
Google publishes its own guidance on AI features in Search on Google Search Central. We point clients there when they ask what Google itself says, because it is the authoritative source and no acronym overrides it.
What we actually measure
We use the same measurement set under every label, and we run it in Promptwatch:
- Prompt tracking. A panel of buyer questions, tracked daily per model, country and language, producing Visibility Score, Share of Voice, Sentiment and Position in Answer.
- Citation analytics. Which pages and domains each engine cites, including Reddit, YouTube and LinkedIn, plus offsite mentions where our client is named without a link.
- Agent Analytics. IP-verified crawler logs for named bots such as GPTBot, OAI-SearchBot, ClaudeBot and PerplexityBot, with a citation rate per page and error tracking.
- Visitor analytics. AI-referred visits and conversions.
- Content tools. Content gap analysis with coverage scores, and the Content Agent on the Business plan for clients who want scheduled drafting with review.
The Action Board then turns the data into a prioritized list, so the discussion with the client is about tasks, not terminology.
A quick mapping we use in kickoff calls
- If the client's goal is "be the direct answer", we start with content structure and test whether pages answer the sub-questions engines search for.
- If the goal is "be named in AI answers", we start with a prompt panel and a competitor benchmark.
- If the goal is "be described correctly", we audit how the brand is described across engines and which sources feed that description.
- If the goal is "show up at all", we check crawler access first.
What we do not promise
We do not promise that any of this produces a recommendation. Engines vary by day and by user, and we cannot control what a model decides to say. What we can offer is a measured baseline, a list of causes worth fixing and trend reporting that shows whether the fixes helped.
If you are writing a plan and stuck on terminology, pick one term, define it in one sentence in the plan, and attach the four measurable outcomes above. That is more useful than choosing the perfect word.