Semantic search

Semantic search retrieves results based on the meaning of a query rather than exact word overlap, so a search for "how to reduce customer churn" can surface a document about "improving retention" even though it shares no keywords. It works by converting both the query and the searchable content into embeddings and finding the closest matches in that vector space, typically stored in a vector database.

This differs from traditional keyword search, which matches literal terms and their simple variants and ranks by term frequency or link signals; semantic search instead ranks by learned similarity of meaning, and many production systems combine both, hybrid search, to get keyword precision and semantic recall together. It is the retrieval mechanism most retrieval-augmented generation systems rely on, and it powers natural language query interfaces that let people search or query data in plain English.

Semantic search matters wherever query wording and document wording diverge, which is common with jargon, synonyms, or paraphrasing. The main pitfall is over-trusting similarity as relevance: a semantically close result is not always the most useful or authoritative one, and semantic search can also surface superficially related but substantively wrong matches if the embedding model was not trained on similar content, an issue closely tied to knowledge graph approaches that encode explicit relationships instead.