Author

aiterms

What is semantic search?

Semantic search is a way to find information by meaning, not just by matching the exact words you typed.

Why it matters

Traditional keyword search is good when you know the exact term to look for. Semantic search helps when users phrase the same idea in different words, use synonyms, or ask a question instead of naming a document.

You’d reach for it when:

• search queries are messy or conversational,
• exact wording varies a lot across documents,
• you want to retrieve “about the same thing” rather than “contains the same string.”

In practice, teams often use semantic search as the retrieval layer for AI apps, document search, and question answering.

How it works

At a high level, semantic search turns text into numerical representations that capture meaning. These are often called embeddings.

1. A model converts the query and each document, passage, or chunk into vectors.
2. The search system compares those vectors to find items that are close in meaning.
3. The closest matches are returned, often with a reranker or filters on top.

This differs from classic lexical search, which mainly scores shared words and phrases. Semantic search can match “how do I reset my password?” with “account recovery steps” even if the words do not overlap much.

Many real systems combine both approaches. A lexical index helps with exact terms, names, and rare tokens; a semantic layer helps with meaning and paraphrase. That hybrid setup is common because pure semantic retrieval can miss exact matches, while pure keyword search can miss intent.

Tiny concrete example

Query: How do I change my email address?

Possible semantic search result:

• Update your account profile
• Change your login details
• Manage personal information

Those documents may not contain the exact phrase “change my email address,” but they are semantically related.

Common pitfalls / when NOT to use it

• Don’t use it when exact spelling matters. Part numbers, error codes, legal citations, and code identifiers are often better served by keyword search.
• Don’t expect perfect recall. Semantic similarity is approximate, so it can miss niche or highly specific matches.
• Don’t skip metadata filters. If you need results from one tenant, region, or time range, use structured filters alongside semantic retrieval.
• Don’t assume it understands truth. It finds related text, not verified answers.
• Don’t replace all search with embeddings. Most production systems work better with hybrid retrieval.

Related terms

Related terms

• What is retrieval-augmented generation (RAG)?
• What is a vector database?
• What is an embedding?
• What is chunking (and semantic chunking)?
• What is hybrid search (BM25 + vector)?
• What is a reranker / cross-encoder?
• What is top-k retrieval?
• What is a knowledge base for RAG?
• What is contextual / late chunking?
• What is RAG vs fine-tuning?

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