Data
Semantic Search
A search approach that finds results based on meaning and intent rather than exact keyword matches.
Semantic search retrieves content based on conceptual similarity rather than exact word overlap. Instead of searching only for literal keyword matches, it tries to understand the meaning behind the query and the documents.
This is usually implemented with embeddings. The query and documents are converted into vectors, then the system finds the nearest matches in vector space.
Example: a search for "ways to cut support backlog" might retrieve documents about ticket triage and automation even if they never use those exact words.
Where Semantic Search Is Useful
- Enterprise knowledge search — find relevant internal docs quickly
- RAG pipelines — retrieve grounding context for LLMs
- E-commerce discovery — match products by intent, not only text
- Recommendation systems — surface conceptually similar items
Semantic search is one of the most important building blocks in modern AI applications. It improves retrieval quality and is often combined with keyword search and reranking for the best real-world results.