What Is Semantic Search?
Semantic search finds results by meaning rather than matching keywords, so 'money back' finds your refund policy. How it works and where it beats keyword search.
Semantic search: search that matches by meaning rather than exact keywords, using embeddings to find the results closest in intent, so a query and a document match even when they share no words.
Semantic search is search that matches by meaning rather than exact keywords, using embeddings to find the results closest in intent, so a query and a document match even when they share no words.
The query is converted to an embedding and compared against embedded content in a vector database; the nearest matches are returned. That’s why ‘refund for a damaged item’ can surface a policy titled ‘returns of faulty goods’: different words, same meaning.
Why it matters
Keyword search fails exactly where users need it most, when they don’t know your exact terminology. Semantic search closes that gap, and it’s the retrieval step that makes RAG assistants and ‘search that actually works’ possible. Often the best systems blend semantic and keyword search.
How Finzarc thinks about it
We build semantic search (and hybrid keyword-plus-semantic) as the retrieval layer under assistants and internal search. See embeddings, vector databases, and talk-to-your-data.
Related
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Questions, answered.
What is semantic search?
It's search that matches by meaning rather than exact keywords, using embeddings to find the results closest in intent, so a query finds the right document even when they share no words.
How is semantic search different from keyword search?
Keyword search matches literal terms; semantic search matches meaning. Semantic wins when users don't know your exact terminology. The strongest systems often blend both, using hybrid search.
Is semantic search the same as RAG?
No. Semantic search is the retrieval step; RAG uses that retrieval to feed a language model so it can generate a grounded answer. Semantic search finds; RAG finds and answers.
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