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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.

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.

What tends to get cited

Observed patterns across answer engines:
  • 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.

Limits

GEO influences retrieval — which pages a model finds and quotes when it searches. It does not change the model’s prior knowledge, the brand associations baked into its training weights. When a model answers from memory without searching, GEO has no effect. Shifting those priors requires sustained third-party presence over much longer periods.