ModelRefs / Semantic Search — AI Glossary
Semantic Search — AI Glossary
Search that matches by meaning rather than keyword overlap, using embedding similarity to find relevant documents. Hybrid search combines both.
Overview
Semantic search retrieves documents whose embeddings are closest to the query embedding. Outperforms keyword search on paraphrase and concept queries; weaker on exact-match and rare-term recall. Hybrid search combines both.
Reference details
| Topic | rag |
|---|---|
| Last reviewed | 2026-06-24 |
Related terms
Example: The query it gets wrong
“Documents that do not mention pricing” — semantic search returns documents about pricing, because negation barely registers in embedding space and the topic dominates. The same weakness explains why “increase” and “decrease” retrieve each other: they are about the same thing while meaning opposites.
Commonly confused with
Semantic search is the goal, not the technique. Dense bi-encoder retrieval is the usual implementation, but sparse methods like SPLADE also target meaning while staying sparse. Conflating the goal with one implementation hides that hybrid approaches exist.
When to use it
Reach for it when:
- Users describe what they want rather than naming it
- Vocabulary differs between query and document
- Concept-level recall matters more than exact matching
Reach for something else when:
- The query is an identifier, code or exact name
- Negation or opposition must be distinguished
- You need an auditable reason why a result was returned
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Semantic Search — AI Glossary.
Frequently asked questions
What is Semantic Search?
Search that matches by meaning rather than keyword overlap, using embedding similarity to find relevant documents.
What concepts are related to Semantic Search?
Closely related concepts include embedding, vector database, hybrid search, bm25.