What is Answer Engine Optimization (AEO)?
Answer Engine Optimization (AEO) is the practice of shaping content so it can be lifted directly as the answer to a question, rather than merely ranked among results. It predates AI assistants — it began with featured snippets and voice search — and now largely overlaps with Generative Engine Optimization.
Updated 19 July 2026
Four names, one problem
The vocabulary here is a mess, and pretending otherwise helps nobody. You will see all of these used as though they were established terms:
- AEO — Answer Engine Optimization
- GEO — Generative Engine Optimization
- LLMO — Large Language Model Optimization
- AI SEO — a catch-all, usually marketing
They are not four disciplines. They are four attempts to name the same shift, coined at different moments by different people, and the boundaries between them are argued rather than agreed.
Here is the most defensible distinction:
AEO is older and narrower. It came from featured snippets, "position zero" and voice assistants — the era when Google started answering questions directly instead of only listing links. Its core technique is structuring content so a machine can extract a clean answer: a direct response near the top, clear headings, question phrasing, structured data.
GEO is newer and wider. It concerns generated answers that synthesise many sources into new prose, where the goal is to be named inside that synthesis. It depends heavily on what the wider web says about you, not just how your own pages are structured.
LLMO is largely a synonym for GEO with a more technical flavour.
AI SEO usually means whatever the person using it is selling.
Why the distinction still matters
Because the techniques diverge at the point that matters most.
AEO is fundamentally about your own pages: make the answer extractable. That is a formatting and structure discipline, and it is largely within your control.
GEO is substantially about everyone else's pages: be the brand that gets named when a model synthesises the category. You cannot format your way to that.
A team that treats them as identical will do the AEO work — good structure, clear answers, FAQ schema — and then be puzzled that assistants still recommend a competitor. The structure was necessary and not sufficient.
What AEO technique actually looks like
The parts that hold up:
- Answer first, elaborate second. Put a direct 40-60 word answer immediately under the heading. Extraction lifts the first clean answer it finds.
- Use the question as the heading. Match the phrasing a person types, not internal jargon.
- One question per section. A section covering three things extracts poorly for all three.
- Structured data where it is genuine. FAQPage, HowTo, Product — marking up content that really exists on the page. Marking up content that is not visible is a policy violation, not a shortcut.
- Plain, declarative sentences. Hedged, clause-heavy prose is hard to lift cleanly.
- Keep the answer current. A confidently wrong answer that gets extracted is worse than no answer.
None of that is exotic. It is unusually well-suited to being checked, which is why the readable half of AI visibility is the half you can scan for free.
Where AEO stops
AEO makes you extractable. It does not make you chosen.
When an assistant answers "who are the best suppliers of X", it is not extracting from one page — it is synthesising an opinion from everything it has absorbed about the category. Your beautifully structured FAQ does not enter that judgement unless the wider web already associates you with X.
That gap is the reason we measure outcomes by asking assistants directly rather than auditing pages and inferring. A page audit tells you whether you could be cited. Only asking tells you whether you are.