GEO, or generative engine optimisation, is the practice of structuring content so that AI answer engines such as Google AI Overviews and ChatGPT cite it in their generated responses. Where traditional SEO chases a blue link, GEO chases a citation inside the answer itself. It is a newer discipline that sits alongside, and increasingly above, conventional search optimisation.
Search engine optimisation is built around getting a page to rank in a list of results. A user still has to click through to read the answer. GEO is built around getting the content absorbed into the AI model's response directly, so the user may never leave the results page at all. The signals overlap, but the goal is different: ranking positions matter less than whether your phrasing, data, or structure becomes the source the model chooses to quote.
GEO-optimised content tends to be tightly structured, answer-first, specific, and grounded in first-hand evidence. Vague or padded prose rarely gets cited because AI models pull from the most quotable, trustworthy passage they can find. That means E-E-A-T signals, clear definitions, and named sources are unusually important.
Google's AI Overview rollout put AI-generated answers at the top of hundreds of millions of queries. Research from Princeton, Georgia Tech, and elsewhere confirms that citation rates differ sharply by content type: pages with original statistics, authoritative quotes, and structured prose are cited far more often than thin, generic content. For publishers and brands, GEO is no longer optional. A page that is never cited in an AI answer is invisible to the growing share of users who never scroll past the generated response.
The practical GEO loop is: identify questions your audience asks AI engines, build content that answers them with verifiable specifics, structure it so a model can extract a clean passage, then monitor citation share rather than just rank position.
Related: Deep dive: generative engine optimisation · What is an AI overview?
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