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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.
Mentioned in
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Advanced New Tail Keyword Research
Show 100 people a photo of your product and ask what they would type into Google. A survey on Zoysia Tenuifolia grass returned 68 terms: 24 already on the top-ranking page and 44 it had never used.
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Selection Rate Optimization
Selection Rate Optimization is the AI-search counterpart to click-through-rate optimization: improving how often AI systems choose your content as the source they ground and cite their answers on.
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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.
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AI Visibility
AI Visibility is the outcome of AI-centric SEO, focusing on being cited and recommended by AI systems through brand mentions and source citations in answers.