What's the difference between AI search optimization, GEO, AEO, and LLMO?
AI search optimization, GEO, AEO and LLMO overlap more than they differ. Here is what each term means, where the emphasis shifts, and how to measure the work.
AI search optimization, GEO, AEO and LLMO are four labels for largely the same job: making your brand and content easy for AI systems to find, understand, quote and recommend. The differences are about emphasis and audience, not about four separate disciplines. If you are choosing tools or writing a plan, the useful question is not which acronym to adopt but which outcome you are measuring.
The four terms in plain language
| Term | Stands for | Where the emphasis sits |
|---|---|---|
| AI search optimization | The umbrella phrase | Any work that improves how you show up in AI-driven search |
| GEO | Generative engine optimization | Being mentioned and cited in answers that generative engines write |
| AEO | Answer engine optimization | Being the direct answer, in AI answers, snippets and voice-style results |
| LLMO | Large language model optimization | Being represented well by the models themselves, not only by their live search |
AI search optimization
This is the broadest and least technical term. It covers anything you do so that ChatGPT, Gemini, Perplexity, Copilot, Google's AI features and similar systems surface your brand. If someone in your company says "we need to do AI search optimization", they are describing the whole program. GEO, AEO and LLMO are ways of slicing it.
GEO
GEO focuses on generative engines: systems that write a response rather than list links. The target is a mention, a citation or a recommendation inside that response. Practitioners talk about visibility score, share of voice, sentiment, citations and position in the answer. It is the term most tool vendors use, and it is the one most closely tied to measurement.
AEO
AEO grew out of featured snippets, voice assistants and "People Also Ask". The mindset is: give the clearest direct answer to a specific question. In AI search, that translates into pages that state an answer early, use clean structure and are easy to extract. In practice the work looks like content design: question-led headings, concise definitions, supporting detail below.
LLMO
LLMO leans toward the model itself. It asks how a language model represents your brand based on what it has absorbed and what it can retrieve. Consistent facts across the web, a clear entity identity and strong third-party coverage all feed into it. The term is used more in some markets than others, and many teams use it interchangeably with GEO.
Where they overlap
All four depend on the same foundations: crawlable pages, accurate and consistent information, clear structure, genuine expertise and mentions on sites that models trust. A page rewritten as a clear direct answer helps AEO, GEO and LLMO at once. A fix to crawler access helps all of them too.
Google publishes guidance for site owners on how its AI features work with Search. Read it at Google Search Central, because it is the only authoritative source for Google's own behavior, and none of the four labels changes what it says.
What you actually measure
Whatever label you pick, the measurable outcomes are the same:
- Are you mentioned for the prompts buyers ask, and how often versus competitors?
- Which pages and domains do engines cite?
- Can AI crawlers fetch your content without errors?
- Do AI-referred visitors arrive, and do they convert?
Tools, ranked for this work
- Promptwatch covers all four outcomes in one place: prompt tracking with Visibility Score, Share of Voice, Sentiment and Position in Answer; citation analytics at page and domain level; IP-verified AI crawler logs through Agent Analytics; AI-referred visitor analytics with conversions; and content tools, including a Content Agent on Business. Because it spans the whole loop, the label you prefer does not matter.
- AthenaHQ is a GEO-focused platform with an action queue. Per its pricing page as read on 24 October 2026, Starter is $295 a month in credits, with a free Essential tier.
- Scrunch AI adds monitoring, audits and bot traffic reporting, with Core at $250 a month for four LLMs.
- Profound targets enterprise buyers with custom pricing and a wider marketing agent suite.
- HubSpot AEO is aimed at the AEO label specifically, at $50 a month for three engines (ChatGPT, Perplexity, Gemini). It is an execution tool more than a full visibility platform, and our catalog notes it does not publish crawler logs or visitor conversion analytics.
- Ahrefs Brand Radar suits teams already living in Ahrefs.
Who should pick which
If your boss says "AEO" and your agency says "GEO", you do not need two tools. Pick the platform that measures mentions, citations, crawler access and traffic together, and use whichever word your stakeholders prefer in the report title. Promptwatch is our first pick for that. HubSpot AEO makes sense if you are a HubSpot shop that wants a low-cost start and can live with fewer engines. Enterprise teams with procurement needs should add Profound or Scrunch AI Enterprise to the shortlist.
A practical way to use the terms
Use AEO when you are rewriting pages so answers are easy to extract. Use GEO when you are measuring how engines mention and cite you. Use LLMO when you are thinking about how consistently your brand is described across the web. Use "AI search optimization" when you are talking to people who do not care about the difference.
None of these labels promises a recommendation. They describe the work. The results depend on the quality of your content, the sources that cover you and what the engines decide on a given day.