Search that matches a query to content by meaning rather than exact keywords, comparing vector embeddings; the retrieval method behind AI answers.
Semantic Search matches a query to content by meaning rather than by exact keywords. It converts both the query and the candidate text into vector embeddings, then ranks results by how close those vectors sit, usually measured with cosine similarity. A page can rank for a query it never uses verbatim, as long as it means the same thing.
This is the retrieval method behind AI answers. When a system runs a live search to ground a response, vector search over embeddings is how it finds relevant passages, often combined with older keyword matching. Because the match is on meaning, covering a topic thoroughly and unambiguously matters more than repeating a target phrase, and matching the intent behind a query matters more than mirroring its words.
Semantic search rests on the idea that meaning has geometry: related concepts land near each other in embedding space, a property the manifold hypothesis describes. Making content easy to place correctly in that space, through clear, specific, well-structured writing, is part of being retrievable by an AI system.
