Generative Engine Optimization is a sibling discipline to AEO focused on the phrasing, structure, and evidence patterns that make LLM answer engines quote a source verbatim.
Generative Engine Optimization (GEO) is a sibling discipline to AEO focused on the phrasing, structure, and evidence patterns that make LLM answer engines quote a source verbatim.
In practice GEO and AEO overlap heavily; where they differ, GEO leans toward content craft (self-contained sentences, explicit attributions, quantified claims) while AEO leans toward the machine-readability stack (schema, canonical, entity graph).
See our deep dive: What is GEO?
Generative Engine Optimization (GEO) is the practice of shaping content so large-language-model answer engines quote it verbatim inside their generated responses — a sibling discipline to AEO that focuses on the phrasing, structure, and evidence patterns that models prefer to lift.
SEO ranks a page in a list of results; AEO gets the page cited inside a synthesized answer — same crawl foundation, different measurement, different structured-data emphasis, and a different set of engines that decide who wins.
A first-sentence direct answer wins in AI results because grounded answer engines lift stand-alone quotable sentences into their synthesized responses — a full sentence stating the fact is retrievable and citable, while a bullet fragment or a lead-in paragraph forces the model to paraphrase, which reduces the odds it credits the source.