Watch: 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.
Transcript
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.