AI Visibility
Generative Engine OptimizationGEO
The subset of answer-engine optimization focused specifically on earning citations inside generative answers produced by large language models.
By Shimon Carroll, Founder, SEO for AI Agents · Last updated
Generative Engine Optimization is the discipline of being cited by a generative model when it composes an answer. Where classic SEO optimizes for a ranking position, GEO optimizes for inclusion in the model output and for the way the model attributes the claim back to a source.
The foundational academic reference is the 2023 paper "GEO: Generative Engine Optimization" by Aggarwal and colleagues, which ran controlled experiments showing that adding cited statistics, direct quotations, and authoritative language measurably increased a source's visibility inside generative answers, with gains of up to 40 percent on some content types. That result is the empirical backbone of every credible GEO recommendation: the levers that move citation are corroborated facts, quotable phrasing, clear entity association, and being present in the corpora the engines trust.
GEO is not a separate signal you toggle on. It is the same content and entity work that earns trust, expressed in a form a model can extract. The most overstated GEO tactic is llms.txt, a proposed file that no major engine honors; the most underrated is simple corroboration, getting the same fact stated about your brand across Wikipedia, Reddit, review sites, and your own pages so the model sees agreement.