AI Glossary · Search & RAG
Semantic Search
Search based on meaning rather than keywords, powered by embeddings; the retrieval half of most RAG systems.
Definition
What is Semantic Search?
Semantic Search is search based on meaning rather than keywords, powered by embeddings; the retrieval half of most RAG systems.
- Category
- Search & RAG
- Glossary set
- 8 related terms
- Audience
- Enterprise AI leaders
Why does Semantic Search matter in enterprise AI?
Semantic Search matters in enterprise RAG because it affects retrieval quality, answer trust, citation accuracy, and how safely AI systems use governed knowledge.
Related terms in Search & RAG
- Chunking
- Splitting source documents into passages sized for retrieval and the model's context window. Chunk size and overlap are top-3 RAG quality levers.
- Enterprise Search
- Unified search across internal systems (SharePoint, wikis, tickets, CRM) — increasingly powered by embeddings and LLM answers with citations.
- GraphRAG
- RAG variant that retrieves over a knowledge graph and communities of entities; strong for multi-hop questions on connected enterprise data.
- Hybrid Search
- Combining keyword (BM25) and vector search to improve RAG relevance, especially for enterprise content with codes, IDs and jargon.
- RAG (Retrieval-Augmented Generation)
- Fetching relevant documents at query time and passing them to the model so answers cite trusted sources instead of relying on parametric memory.
- Re-ranker
- A second-stage model (cross-encoder) that reorders top-k candidates from initial retrieval for sharper relevance — the fastest RAG quality win.