What Are GEO and AEO? Being Visible in AI Search

Short answer: GEO (generative engine optimisation) aims to get your content used as a source when an AI engine composes its answer. AEO (answer engine optimisation) is structuring content so it answers a question directly and citably. They work together: AEO builds the structure, GEO measures and grows the visibility.

Why a new discipline was needed

In classic search the goal was to be one of ten blue links. The user saw a list, opened a few, compared. In an answer engine there is one answer. You are either in it or you are not. There is no third position.

That carries two messages at once. The bad one: very few sources win. The good one: the field is nowhere near as crowded as Google, so the return on moving early is measurably high today.

Three concrete differences from SEO

1. The metric changes. SEO measures ranking; GEO measures mention rate — how often, and in what context, your brand appears across a defined set of questions.

2. Page structure changes. A search engine evaluates a page as a whole; an answer engine takes a passage. So the answer must sit near the top, in a paragraph that stands on its own. An answer delivered at word 800, after a long warm-up, does not get cited.

3. The authority signal changes. In classic SEO, links dominate. In answer engines, verifiability comes forward: clear definitions, sourced facts, a consistent entity definition and no contradictions between pages.

What is actually done

Answer-first writing

Every important page answers its main question in the first paragraph, understandably on its own. The “Short answer” block at the top of this article exists for exactly that reason.

Q&A blocks and FAQPage schema

Putting real questions on the page with real answers, and marking them up so the engine reads the question–answer relationship rather than inferring it.

Entity clarity

The engine must recognise your brand as an entity. That takes Organization / Service schema, a consistent name–address–phone across every platform, and sameAs links. A brand name written differently in different places produces two separate entities in the engine’s model.

Crawler access: robots.txt, llms.txt, llms-full.txt

AI crawlers (GPTBot, PerplexityBot, ClaudeBot, Google-Extended and others) are governed by robots.txt. Blocking them makes you invisible in answer engines — a simple mistake, made often. Beyond that, llms.txt gives a structured summary of the site and llms-full.txt a plain-text version of all content, so an engine can read the site without issuing 300 separate requests.

Measurement

A defined question set is run against the engines on a schedule, tracking mention rate, the context of each mention and how it compares with competitors. Working in this field without measurement is guesswork.

Four common mistakes

1. Blocking AI crawlers in robots.txt. Trying to stop content being “taken” while also closing off visibility.
2. Burying the answer. SEO habits produce a long intro with the answer somewhere in the middle.
3. Publishing unreviewed AI content. It produces both factual errors and a brand voice with no character — and engines do not cite repetitive, undifferentiated text.
4. Unverifiable claims. “The best in the country” adds nothing to citability. One provable data point is worth ten adjectives.

Who should prioritise this?

Any category where people research before deciding: B2B services, healthcare, finance, education, software, consultancy and high-consideration products. If the user is asking “which one should I choose”, being inside that answer is the sale.

Sources

• Google Search Central — AI features and your website
• Schema.org — FAQPage
• OpenAI — GPTBot documentation