GEO and AEO Explained: How to Get Cited by AI Assistants

GEO (Generative Engine Optimisation) covers the practices that get a piece of content cited inside an AI-generated answer. AEO (Answer Engine Optimisation) is the narrower goal of being the direct answer to a specific question, whether inside an AI summary or a classic featured snippet. Both terms describe the same shift: being read by a machine that writes, not just found by a human who clicks. A conversational assistant is not yet an AI agent in the strict sense, but it shares the same basic building block: fetching scattered information before it answers.

What do GEO and AEO mean, and how do they differ from classic SEO?

SEO (Search Engine Optimisation) optimises for rank: showing up as high as possible in a list of links. GEO and AEO optimise for citation: appearing as a source inside a written answer that the user may never click through to verify. That is a shift in goal, not just in terminology.

This is not a zero-sum game against SEO. An AI assistant builds its answer from pages a search engine has already indexed and judged relevant. GEO and AEO sit on top of SEO fundamentals (speed, structure, authority), they do not remove the need for them.

How does an AI assistant decide what to cite?

This is the least understood part, and the most useful to know. An assistant such as ChatGPT does not search on the exact sentence a user typed. It first rewrites that question into one or more internal search queries, different from the original wording, runs them, reads the pages returned, then writes its answer from what it found.

These internal queries are not hidden: they show up in the data the browser downloads to render the answer, which is how they were first observed directly. In practice, a business owner who asks "which AI consultant should I use" does not trigger a search on that exact phrase, but on rewordings the assistant builds on its own, often narrower or broader than the original question.

The direct consequence: content is no longer written only for the user's question, it is written for the queries the machine asks in its place. A page that answers a broad intent but stays vague in its wording has less chance of matching one of these rewordings than a page that names precisely what it does, for whom, and under what conditions.

Why does being inside the rewritten query matter more than being merely findable?

An independent analysis, published by its author on the blog suganthan.com, suggests that being present inside the query an assistant rewrites is worth roughly 33 times more, in terms of final citation likelihood, than simply being indexable and findable through a classic search engine. That figure needs a firm caveat: the sample covers only around sixty conversations, it has not been peer-reviewed, and it should be read as an illustrative order of magnitude, not an established measurement.

What holds regardless of the exact number is the mechanism itself: query rewriting is a distinct step, separate from classic indexing, and it filters what has any chance of reaching the final written answer. A page can be fully indexed and still absent from every plausible rewording of a question, simply because it does not name what it covers precisely enough.

This connects to a wider pattern: the generative layer is becoming a reading intermediary between a business and its customer, whether that is a Google summary or a ChatGPT answer. Text is no longer just read, it is first re-read, synthesised, and sometimes reformatted by an AI before it reaches its destination.

How do you write for the query the machine asks itself?

This is where GEO genuinely departs from good web writing. The target is no longer the sentence your customer typed, it is the query the assistant builds from it. That query is almost always shorter, more generic and more normalised than the original question.

Three practical consequences follow, none of which has an equivalent in classic search optimisation.

The rewording vocabulary matters more than your customer's vocabulary. A director writes "I want my accounting software to stop eating my evenings". The assistant will search for something closer to "automated invoice data entry" or "supplier invoice OCR". A page carrying only the customer's emotional wording, or only the vendor's jargon, misses the rewrite in both directions. Both registers need to sit on the same page.

A page that answers three questions wins none of them. The internal query is a single question. One long page covering funding, method and tooling maps poorly onto each of the three queries, where three clean pages can capture three. That is the opposite of the classic instinct to consolidate in order to concentrate authority.

There are two filters, not one. Entering the set of queries the machine asks itself is the first. Surviving the sort that decides which sources actually get cited in the answer is the second, and it is harsher. The first filter is worked through vocabulary and page splitting, the second through what makes a source safe for an engine to quote: an attributable claim, a date, an identifiable author.

One thing none of this compensates for: the business name and the actual profession have to appear in the text, in words. A logo, a graphic signature or a footer set in an image is not read.

Should traditional search optimisation be dropped in favour of GEO?

No. A site absent from classic search results has very little chance of being pulled into a generated answer, because the model draws first from pages a search engine already treats as relevant. If you operate in France, Google's rollout of AI Overviews on 22 July 2026, one of the last major markets to get it, is a clear example of this shift, though the full detail of that rollout sits outside the scope of this article.

GEO and AEO add a writing discipline on top of SEO, they do not replace it. Getting cited by an assistant starts with the same discipline that makes any page worth reading: naming what you do, answering plainly, and backing claims with a date and a source.