Relevance Engineering
Engineering a page's semantic relevance to a query with embeddings and vector math, treating visibility as an engineering problem rather than keyword optimization.
Relevance Engineering is the discipline of engineering how relevant a page is to a query, using embeddings and vector math rather than keyword tuning. Topics, pages, and queries are represented as vectors, and relevance is measured as the closeness between them. It treats search visibility as an engineering problem with a measurable target, not an optimization exercise. The term was coined by Mike King of iPullRank.
It is the technical method beneath AI Visibility, and is described in full in the article Relevance Engineering.