Relevance Engineering

Relevance Engineering is the practice of deliberately building a page's relevance to a query, using the same semantic machinery that search and AI systems use to judge it. Where traditional SEO tuned keywords, titles, and links, relevance engineering works with meaning directly: topics, pages, and queries are turned into embeddings, and relevance is measured as how close those vectors sit together.

The shift in the name is the point. The discipline treats search visibility as an engineering problem rather than an optimization exercise. You build toward a measurable target, semantic closeness, instead of nudging signals and hoping. The term was coined by Mike King of iPullRank.

How it works

1. Meaning, not keywords. Content and queries are represented as vectors that capture meaning, following the move from lexical to semantic search.
2. A center for each topic. Each core topic is represented by an average, central vector, so any page can be scored against it.
3. Closeness as a score. The distance between a page's vector and its target topic gives a relevance score, rather than a guess.
4. Decisions follow the score. Those numbers drive what to write, what to cut, how to cluster pages, and how to link them. Expertise can be measured the same way, by averaging the vectors of everything an author or site has published.

Relevance Engineering is the technical method beneath AI Visibility: making a page genuinely, measurably relevant is how it earns a place in the answers AI systems generate.

Related concepts

• Relevance Engineering
• AI Visibility