Semantic search: definition

Semantic search finds results by meaning rather than exact keywords, so a search for "cancel my plan" surfaces the "how to end your subscription" page even though they share no words.

Traditional search matches words. Semantic search matches meaning, using embeddings to compare the intent of the query against the intent of each document. It is why a good AI search understands what you meant, not just what you typed.

It powers the retrieval half of RAG, better on-site search, and smarter internal knowledge tools. In practice it is often combined with keyword search for the best of both.

Related terms

Embeddings: An embedding is a list of numbers that captures the meaning of a piece of text, so that passages about similar things sit close together and can be found by similarity rather than exact words.
Vector database: A vector database stores embeddings and finds the closest matches to a query vector quickly, which is what lets a RAG system retrieve the right passages from millions of documents in milliseconds.
Retrieval-augmented generation (RAG): Retrieval-augmented generation is a technique that fetches relevant passages from your own documents at question time and hands them to the model, so its answer is grounded in your data instead of its training.