Watch: Relevance Engineering
Relevance Engineering is the practice of building a page's semantic relevance to a query with embeddings and vector math, treating search visibility as an engineering problem rather than keyword optimization.
Transcript
Search engine optimization is undergoing a major shift, moving away from just tuning keywords and links toward something much more precise: relevance engineering. Coined by Mike King of iPullRank, this discipline treats search visibility as an engineering problem rather than an optimization guessing game.
Instead of chasing changing algorithms, relevance engineering works with meaning directly, using the same semantic machinery that modern search engines and AI systems use. It translates content, queries, and topics into mathematical vectors. In this system, relevance is measured by how close these vectors sit to one another in a shared conceptual space.
To make this work, each core topic is given a central vector representing its average meaning. Any page can then be scored against this center. The closer a page's vector is to the target topic, the higher its relevance score. This score removes the guesswork, giving content creators a clear, measurable number to guide their decisions on what to write, what to cut, and how to link pages together. You can even measure author expertise by averaging the vectors of everything a writer has published.
Ultimately, relevance engineering is the technical foundation of AI visibility. By making a page genuinely and measurably relevant, you ensure it earns a spot in the answers generated by modern AI systems.