What is generative engine optimization (GEO)?
Generative engine optimization, or GEO, is the practice of making a brand easier for AI answer engines to mention and cite. It covers the questions buyers ask, the pages that answer them, and the independent sources those engines already use. GEO sits beside ordinary search work, and no checklist guarantees a citation.
Why it matters for AI visibility
Buyers now ask ChatGPT, Gemini, Perplexity, Claude and Google AI features for recommendations, comparisons and how-to answers. Those answers are assembled from pages the engine can retrieve and from what the model already learned. GEO is the name for the work of showing up in that assembly: clear answers on your own site, consistent product facts, and genuine mentions on sources the engines already cite. It matters because a strong classic ranking does not, by itself, tell you whether an assistant names you. Teams treat GEO as a measurement habit plus an editorial habit. They keep a stable prompt list, read the actual sources under each answer, and improve the pages and third-party coverage that keep failing to appear. The goal is evidence a buyer can check, not a promise that a model will prefer one brand.
Example
A payroll company notices that prompts such as “best payroll software for a 40-person agency” name three rivals and cite two review roundups. None of those pages mention the company. The GEO work is to answer that question on a comparison page and to earn a factual mention in one of the roundups buyers already see cited. The next check uses the same prompt and records whether the answer changed.
Related terms
GEO overlaps answer engine optimization, which is the broader habit of winning the answer rather than only the blue link. LLM visibility is how you observe the result, and an AI citation is the linked source inside an answer. GEO is the practice; those terms are the measurements and the outcomes.