What is semantic search?
A search approach that ranks results by meaning and intent rather than relying only on exact keyword overlap, commonly using embeddings, learned rankers, or hybrid retrieval.
Why is this important?
Semantic search improves discovery when users describe concepts differently from the source material. It can retrieve relevant knowledge across natural language, but should not replace exact matching for identifiers, names, or regulated terms.
How it works
The system interprets the query, generates a semantic representation, retrieves candidate items from a vector or neural index, applies filters, and may rerank results alongside lexical scores.
Technical example
A query for reducing customer churn retrieves content about retention risk, renewal health, and cancellation prevention even when the phrase customer churn is absent.
Implementation notes
Evaluate relevance with real queries, tune score thresholds, retain keyword and metadata filters, monitor embedding drift, prevent cross-tenant leakage, and use hybrid search for precision-sensitive workloads.
