What is generative engine optimization?
GEO, or generative engine optimization, concerns how content is presented in answers produced by generative AI systems and how changes to that content may affect its visibility.
The original GEO paper proposes a framework for content optimization and visibility evaluation. In a practical example, someone asks an AI service for child-friendly cafes. An observer can record which cafes are mentioned, which pages are cited and whether the service details are correct. Those are separate observations.
A definition is not a proven optimization method
Different questions, products, models and dates can produce different answers. A citation after an edit cannot, by itself, show that the edit caused the citation.
A comparison needs retained questions, full responses, model information, dates and consistent decision rules. Keep missing or failed observations visible, and state the denominator for any rate.
SEO provides foundations for accessible, understandable pages. GEO raises additional questions about generated answers. This definition page is not a step-by-step claim that a specific tactic will increase exposure.
MCAC is LS-Labs' own diagnostic framework for separating mentions, citations, accuracy and action information. It is not a universal GEO scoring standard. The AI visibility measurement guide develops the practical measurement design.
Sources and scope
Source-language entry updated 2026-10-22; no separate source-check date is recorded. This adaptation preserves that provenance and does not represent a new audit of the cited pages.