Engineering a page's semantic relevance to a query with embeddings and vector math, treating visibility as an engineering problem rather than keyword optimization.
For years, getting a webpage to rank on search engines felt like a game of keyword tuning. But a new discipline called relevance engineering is changing the rules. Coined by Mike King of iPullRank, this approach treats search visibility as a precise engineering problem with a measurable target, rather than a guessing game.
Instead of just matching keywords, relevance engineering uses advanced AI embeddings and vector math. It translates topics, web pages, and search queries into mathematical vectors. How relevant a page is to a query is then measured by how close those vectors are to each other in a multi-dimensional space.
This technical method is the driving force behind what is known as AI visibility. By treating search relevance as a measurable, engineering challenge, it allows creators to build content that aligns perfectly with how modern search engines actually understand the world.
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.
