Glossary
Semantic Search
Search that matches by meaning rather than exact keyword overlap — typically backed by vector embeddings — and the retrieval layer most AI engines rely on.
Semantic search uses vector representations of text to find passages similar in meaning to the query, not just matching keywords. A query for "how to write content AI engines cite" can retrieve a page titled "Citable claims for GEO" even when the exact words don't overlap.
How is semantic search different from traditional keyword search?
Keyword search ranks by term frequency, position, and link signals — exact-match driven. Semantic search ranks by embedding similarity, which captures synonymy and paraphrase. Most modern systems combine both (hybrid search).
Does semantic search reward different content?
Yes. Where keyword SEO rewarded literal phrase matches, semantic search rewards conceptual coverage — clear definitions of the entity, explicit relationships between concepts, and natural language phrasing that aligns with how users actually ask questions.
How do you optimize for semantic retrieval?
Lead each section with the question or claim in plain language, define entities explicitly the first time they're mentioned, and avoid keyword-stuffing — which can actually hurt semantic similarity scores.
Part of the Cite Hustle GEO glossary — definitions for generative engine optimization and AI search.