Generative engine optimization (GEO)

Generative engine optimization, or GEO, is the practice of optimizing content to be selected, cited or summarized by AI-generated answers, in tools like AI chatbots and AI-generated search overviews, rather than only to rank as a link in a traditional search engine results page listing. It is an emerging discipline, and its name and definition are not yet fully standardized.

These AI answer systems typically work by retrieving relevant web pages and using them as grounding sources for a generated response, an approach related to retrieval-augmented generation in a large language model. This differs from classic SEO in what "winning" means: traditional SEO targets a ranking position in an ordered list, while GEO targets being one of the sources an AI system chooses to cite, which appears to favor clear, well-structured, directly quotable statements and demonstrated topical authority, though the actual selection criteria are proprietary and unpublished.

GEO complicates attribution: a citation inside an AI-generated answer may never produce a click, so organic traffic alone increasingly understates real visibility, pushing some practitioners toward tracking brand mentions directly. Because the field is new and the underlying platforms change quickly, with no vendor publishing its citation logic, much published "GEO best practice" advice is unverified and platform-specific; treat it as provisional, unlike the more mature, better-documented understanding of how search intent shapes traditional rankings.