GEO (Generative Engine Optimization)

GEO is what SEO became when search stopped being a ranked list of blue links and became an answer assembled from cited sources. The end goal is no longer "rank in position 1"; it is "be the source the AI engine quotes when a user asks the question."

The mechanics differ from classical SEO in three ways. (1) Engines pick a small, ranked set of sources to ground each answer, then quote and link them, typically three to seven citations per answer. The probability you appear there is the new ranking metric. (2) The grounding model rewards structured, factual, well-attributed content with clear semantic anchors (schema.org, heading hierarchy, definitional clarity). Keyword density and link graphs matter less; entity clarity matters more. (3) The query distribution is different: AI engines absorb the long tail of conversational, multi-clause queries that traditional search did poorly on. The questions you optimize for are sentences, not keywords.

In practice, a GEO program looks like: a per-page methodology and dating discipline; schema.org markup including Person, Organization, DefinedTerm, Article, and FAQ; an llms.txt and llms-full.txt to give engines a fast-path map of your site; entity consistency across properties (one author, one organization, linked sameAs profiles); and content that answers questions directly in the first paragraph with an explicit, citable claim.