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Generative engine optimization (GEO) is the practice of making a website more likely to be retrieved and cited by AI systems that generate answers rather than return a list of links — ChatGPT, Perplexity, Google AI Overviews, Claude, and Gemini.

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Also called AEO and LLM SEO

Answer engine optimization (AEO) and LLM SEO describe overlapping practices, and the terms are often used interchangeably. The useful distinction: AEO predates generative engines — it grew out of optimizing for featured snippets. In practice the techniques converge: all three reward direct, factual, well-structured content, so this page covers them together.

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How it differs from SEO

Classic SEO optimizes for ranking in a list of results. GEO optimizes for inclusion in a generated answer. The practical differences:
  • The unit of success is a citation, not a position. There is no rank 1. A page is either drawn into the answer or it is not.
  • Retrieval happens per-query, at answer time. Most answer engines run a live search before responding, so the pages they cite are chosen fresh each time rather than from a fixed index position.
  • Results are volatile. The same question asked twice can produce different sources. GEO is measured as a rate across many queries over time, not by a single result.
  • Passage-level clarity matters more than page-level authority. A model extracts a specific claim. Pages that state facts plainly and early are easier to extract from than pages that build to a conclusion.

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How to write for AI answers

  • Answer in the first sentence. State the answer or the fact, then explain. A fact in sentence one survives extraction; a fact in paragraph six may not be reached.
  • One question per page or per section. A heading that matches the question, followed immediately by the answer.
  • Self-contained passages. Retrieval often works on sections, not whole pages, so each section should make sense lifted out of the page.
  • Be unambiguous about the subject. Repeat the product or concept name rather than relying on “it” across paragraphs.
  • State constraints and limits. Models reproduce caveats when they are stated plainly, which improves answer accuracy about your product.
  • Keep URLs stable. A moved page loses whatever retrieval history it accumulated.
  • Structured data where the content type supports it — Product, Organization, BreadcrumbList, Article. Google stopped showing FAQ rich results in May 2026 and HowTo rich results in 2023, so FAQPage and HowTo markup no longer earns a result on Google; the question-and-answer content itself still matters for AI answers. See structured data for AI search.

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What tends to get cited

In our experience, these tend to get cited:
  • Pages that define one specific thing and say what it is in the first sentence
  • Documentation and help-centre content, which is factual and stable
  • Pages that state real numbers — limits, intervals, prices, thresholds
  • Pages that sit alongside the primary specification they implement
Marketing pages, listicles, and “ultimate guides” are cited comparatively rarely. Persuasive writing gives a model little to extract.

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Limits

GEO influences retrieval — which pages a model finds and quotes when it searches. When a model answers from memory without searching, GEO has no effect. Mentions come from the model’s training data and move slowly; citations come from live retrieval and respond to content — see AI visibility.