Semantic Search
Search that matches meaning instead of exact words — ask in your own phrasing and find the document that answers, even when it uses different vocabulary.
Production AI is a system: context, tools, permissions, traces, evals, and feedback loops around the model.
What it is
Classic search matches keywords; semantic search matches intent. It embeds the query and the corpus into the same vector space and returns what lands closest in meaning. 'How do I get my money back' finds the refunds policy even though the policy never says 'money back'.
Why it matters
People don't know your internal vocabulary. Semantic search closes the gap between how a question is asked and how the answer was written — which is exactly the gap that makes intranet search frustrating and AI assistants useful.
How it works
Semantic search alone misses exact identifiers — product codes, error strings, names — so production retrieval is hybrid: a semantic leg and a keyword leg (BM25 or Postgres full-text) fused with reciprocal rank fusion, often reranked. Semantic search is one leg of the pipeline, not the whole answer.
Related resources
Numeric representations of text (or images) where similar meaning lands close together — the mathematics that lets a system search by meaning instead of keywords.
How an AI agent finds the right document, chunk, or row to ground its answer in — and why the part that matters is the pipeline around the database, not the database itself.
The search behind your AI combines two ways of looking — by exact words and by meaning — then gives the best candidates a careful second read, so answers that exist in your documents actually get found.
The pattern where an AI agent retrieves relevant context from your data before generating an answer — instead of relying only on what the model learned during training.