# Semantic search

**Source:** https://promtable.com/glossary/semantic-search

> Semantic search finds documents by meaning rather than keyword match, using embedding similarity in a vector space.

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Semantic search finds documents by meaning rather than keyword match, using embedding similarity in a vector space.

Traditional keyword search (BM25, TF-IDF) matches strings. Semantic search embeds the query and corpus into vectors and ranks documents by cosine similarity. The result: queries like 'how do I cancel my plan' match documents about 'subscription termination' even with no shared words. Production stacks in 2026 almost always use hybrid search — semantic plus keyword — and re-rank the top-K with a separate model, because pure semantic search misses exact-match terms (product SKUs, error codes).

## When to use

- Search over long-form content.
- RAG retrieval.
- Recommendation by similarity.

## Common mistakes

- Skipping hybrid + re-rank — pure semantic search misses exact terms.
- Re-embedding the whole corpus on every prompt change — only the query side changes.

## Related terms

- [embeddings](https://promtable.com/glossary/embeddings)
- [vector-database](https://promtable.com/glossary/vector-database)
- [rag](https://promtable.com/glossary/rag)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/glossary/semantic-search
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