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Keyword search vs semantic search

Matching words, or matching meaning?

The short answer

Keyword search finds documents containing the words you typed. Semantic search finds documents that mean the same thing, even with no shared words. Each fails where the other succeeds, which is why serious systems run both.

Keyword search

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In one sentence: A search method that finds documents by matching query words to indexed text and ranking the matches, often with scoring methods such as BM25.
Why it matters: Non-specialists often meet keyword search inside document portals, website search bars and AI systems that retrieve sources before drafting an answer. If you know how it works, you can judge why a result appeared, why a relevant document may have been missed, and when exact wording matters. This helps with practical choices: whether to improve the query, add synonyms, clean the document collection, or combine keyword search with semantic search. It also helps you ask better governance questions about what corpus was indexed, how ranking works, and whether staff might over-trust the first result.
The everyday version: It finds documents by looking for the words you typed and then puts the strongest word matches near the top.
The one thing to remember: Keyword search ranks documents by matching query words to indexed text. BM25 improves that ranking, but it does not by itself recognise the same idea expressed in different words.
The commonest misconception: Keyword search and semantic search are the same thing with different names. They are related but different. Keyword or lexical search relies mainly on matching words or terms in text. Semantic search tries to retrieve by meaning, often using embeddings. Many systems combine both because each catches cases the other misses.

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Semantic search

Search, Embeddings and RAG · Starter

In one sentence: A search method that finds material by similar meaning, not just shared words, and returns the most relevant passages for review or further AI use.
Why it matters: People rarely search using the exact words found in a document. That matters in policy, programme and public information work, where the right source may use different terminology, abbreviations or phrasing. Semantic search can help users find the relevant passage faster, which supports better decisions and more transparent answers. It is also useful before an AI system drafts a response, because retrieving relevant source text can reduce unsupported output. But it is not magic: if the index is poor, the passages are badly split, or the topic needs exact legal wording, results can still be wrong or incomplete. Non-specialists should care because search quality shapes what evidence is seen first.
The everyday version: It is a way to search by meaning. Instead of looking only for the same words, it tries to find text that talks about the same idea.
The one thing to remember: Semantic search retrieves passages with similar meaning, even when they use different words. The results still require review in their original context.
The commonest misconception: Semantic search replaces keyword search completely. Not always. Meaning-based matching is helpful when wording varies, but exact keywords still matter for names, article numbers, codes, legal clauses and rare technical terms. Many practical systems combine both approaches.

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