What is answer engine optimization (AEO)?

Answer engine optimization (AEO, also called generative engine optimization or GEO) is the practice of increasing how often AI assistants like ChatGPT, Perplexity and Gemini name or cite your brand in their answers. It overlaps heavily with SEO — the engines read the same web — but the win condition is different: you are trying to be quoted inside one synthesized answer, not ranked in a list of ten links.

Last updated 2026-07-28

How is AEO different from SEO?

The mechanics overlap; the win condition and the feedback loop do not. SEO wins by ranking a page; AEO wins by being named in an answer that cites three or four sources — which means most of the work happens on pages you do not own.

What actually moves AI citations?

Presence in the sources an engine already trusts for your category, expressed in claims an engine can lift and attribute. Nothing else reliably moves the number, and several popular tactics move it not at all.

• Clear, factual, liftable statements about what your product is and who it is for. Engines quote sentences that answer the question directly; they discount marketing adjectives, which carry no extractable claim.
• Presence on the third-party pages engines already cite for your category: comparison articles, review directories, community threads, and category roundups. For discovery questions these outweigh your own site substantially.
• Consistent naming across the web. If your brand is spelled three ways, both the engine's attribution and your own measurement degrade — a matcher looking for one string will miss the other two.
• Crawlable, current content. Most engines ground with live search, and a page that is blocked, JS-only, or a year stale is a page that cannot be cited.
• Structure that survives extraction: real headings that ask the question, a direct answer in the first sentences, and tables for anything comparative. Tables in particular get lifted into answers close to verbatim.

Why does measurement have to come first?

Because AEO advice without measurement is astrology. Every claim about what engines favour is a hypothesis about a system that is stochastic, undocumented, and changing weekly — the only evidence that a change worked for your brand is your own tracked prompts moving afterwards.

The honest loop is short: measure which questions you lose and which domains win them, change the smallest thing that could plausibly matter, then watch your Share of AI Answer over the following weeks. Skipping the first step means you cannot tell a successful change from ordinary variance, and skipping the last means you keep doing whatever you happened to do before a good week. Start with Share of AI Answer, then work the playbook in how to increase your AI visibility.

Is AEO worth doing yet?

It is worth measuring now and optimising selectively. The cost of measurement is small and the information compounds — a prompt set you started tracking six months ago is an asset you cannot retroactively create. The cost of aggressive optimisation is real, and much of the advice circulating is untested.

The sober position: treat AEO as an extension of work you already do rather than a new discipline with a new budget. If you have decent SEO, you are most of the way there. What you are missing is the feedback loop — the metric your existing reports do not contain — and a shift of effort toward the third-party surfaces that decide discovery answers. See AI SEO vs traditional SEO for where the two genuinely diverge.

What does AEO not let you do?

It does not let you guarantee a citation, buy one, or trigger one with a keyword. Engines synthesize from many sources, weight them in ways nobody outside the labs can inspect, and change that behaviour continuously without notice.

Anyone promising guaranteed AI recommendations is describing something that does not exist. What is genuinely available is the same thing SEO offered before it was oversold: systematically better odds, plus honest measurement of whether the odds actually improved.