Semantic Search

Traditional keyword search matches words. If you search for "employee termination process," it looks for documents containing those exact words. Semantic search understands that "how to offboard someone" means the same thing and returns those documents too.

This works because both the query and the documents are converted into embeddings (numerical vectors) by the same model. Similar meanings produce similar vectors. The search system finds documents whose vectors are closest to the query vector, regardless of the specific words used.

The practical impact is significant. Enterprise knowledge bases are full of documents written by different people using different terminology. A policy might say "involuntary separation" while the search query says "firing someone." Keyword search misses this. Semantic search catches it.

Most production systems use hybrid search, combining semantic and keyword approaches. Semantic search is better at understanding intent, but keyword search is better at finding specific names, codes, or identifiers. A hybrid approach uses both signals to rank results, getting the best of each.

Semantic search is the retrieval layer in any RAG system. The quality of your semantic search directly determines whether the LLM gets the right context. If retrieval fails, it does not matter how good your language model is. It will generate an answer based on the wrong information.