The short answer
GEO, AEO and LLMO are three names for one discipline, written from three points of view. AEO is the broadest: appearing in a direct answer on any answer-capable surface, including voice assistants and featured snippets, which existed before generative AI. GEO is the generative subset: being cited inside an AI-written response from ChatGPT, Gemini, Perplexity or Google AI Overviews. LLMO is the same discipline named from the technology side, focused on how a model retrieves and attributes a source.
Their tactics overlap by roughly 80%. In 2026 GEO is the label most teams use, and any agency selling you all three as separate retainers is selling you the same work three times.
What each term covers
AEO: Answer Engine Optimization
The oldest of the three and the widest. It covers any surface that returns a direct answer rather than a list of links: Google featured snippets, People Also Ask, voice assistants, and now the AI answers layered on top of all of them. Because it predates generative AI, its playbook is the most settled: question-shaped headings, a concise factual answer immediately under each one, and structured data that labels what kind of thing the page is.
GEO: Generative Engine Optimization
The subset aimed specifically at model-written responses. The unit of success is not a rank, it is a citation: your domain named inside the answer, ideally with a sentence lifted from your page. That changes the work. Ranking rewards a comprehensive page; citation rewards a page that contains short, standalone, checkable claims a model can attribute without ambiguity.
LLMO: Large Language Model Optimization
The same discipline described in retrieval terms. It asks how a document is chunked, how stable its entities are across sources, and how confidently a model can attribute a claim to it. In practice LLMO work is indistinguishable from GEO work; the term is useful mainly because it points at the mechanism rather than the marketing outcome.
Where they diverge
The distinctions that matter operationally are narrow, and they are about surface and unit of success rather than about tactics.
| Unit of success | Surface | Distinctive lever | |
|---|---|---|---|
| SEO | A ranked link | Google and Bing organic results | Crawlability, relevance, links, page experience |
| AEO | A direct answer | Snippets, voice, AI answers | Question-shaped headings and concise answers |
| GEO | A citation inside a response | ChatGPT, AI Overviews, Perplexity, Gemini, Claude | Entity clarity and third-party corroboration |
| LLMO | Correct retrieval and attribution | The retrieval layer inside all of the above | Chunkable structure and stable entities |
Why the overlap is about 80%
Because all four disciplines depend on the same four foundations, and only the emphasis shifts between them.
- Entity clarity. One canonical description of who you are, what you sell and who you serve, expressed identically everywhere it appears. Models resolve entities, not pages.
- Structured data. JSON-LD that connects into a single graph through cross-referenced identifiers, rather than sitting on each page as an isolated fragment.
- Quotable content. Complete answers in the first two sentences, question-shaped headings, explicit entities, consistent terminology and visible update dates.
- Off-page brand signals. Mentions, reviews, comparisons and community presence on the third-party sources the engines read.
Change any of these and all four outcomes move together. That is the whole argument for running one program instead of three.
What moves all three
The remaining 20% is per-engine work, and it is where most programs fail because they treat every answer engine as one audience. Only about 11% of the domains cited by ChatGPT are also cited by Perplexity. They source differently and reward different things.
ChatGPT leans hardest on external validation: Wikipedia presence and the volume of third-party brand mentions, with communities like Reddit, Quora and G2 acting as social proof. Perplexity behaves like a research assistant, favoring freshness, visible dates and short parseable structure, weighting the last twelve months heavily. Google AI Overviews draws on the Google index filtered through experience, expertise, authoritativeness and trust, and wants a direct answer at the top of each section with sourced figures and consistent Schema.org markup.
Those are three different content calendars, not three names for one.
Which one should you buy
Buy the program, and be suspicious of the label. The questions worth asking a supplier are not about terminology:
- Which engines do you measure, and how many prompts are in the fixed set?
- Will you show me the raw transcripts each month, or a summary of them?
- What is your plan for third-party corroboration, which is the slowest and most important lever?
- What do you consider outside your control, and what will you not guarantee?
An agency that answers those four clearly is doing the work regardless of what it calls it. An agency that cannot has a glossary, not a program.