What is semantic search?
Knowledge, data and integration
Semantic search is a way of retrieving information by meaning and intent rather than exact keyword matching alone. It usually works by converting both content and queries into embeddings, comparing similarity, and then ranking the closest results. In practice, it helps people find relevant documents even when they use different wording, and it often works best alongside keyword and metadata search.
Reviewed by Jackie, Head of Learning & Development, Levellers · Last reviewed 8 June 2026
What this means
Semantic search helps people find information when they know what they mean but not the exact words used in the source material. Instead of matching only literal terms, it looks for related concepts, context and intent.
In practice, that means a search for "board pack from last quarter" can still find a document named "Q4 leadership briefing" even if the phrasing is different. It is especially helpful in internal document collections, where naming conventions are inconsistent and staff queries are often conversational rather than formal.
Most semantic search systems rely on embeddings and a vector index underneath, but the user-facing value is simple: fewer dead ends when people search the way they naturally speak.
Why it matters
Work slows down when a team has the information somewhere but cannot recover it quickly. That is common in policies, project documentation, customer histories, product guidance and prior proposals.
Keyword search still matters, but it struggles when language varies across departments, when users search with broad questions, or when content has been written by many different people over time. Semantic search improves retrieval for these messy, real-world conditions.
It also matters for AI workflows. If a model is meant to answer questions based on internal documents, the retrieval step has to find relevant evidence first. Semantic search is one of the main ways to make that retrieval step more robust.
How it works
The system first creates embeddings for the content it wants to search, often after splitting large documents into smaller chunks. When a user submits a query, the system creates an embedding for that query and compares it with the stored embeddings. The closest items are candidates for the result set.
That candidate set is often improved with reranking, filters and permissions. In many live systems, semantic search runs alongside keyword search in the same request. This hybrid approach is useful because exact product names, policy codes, dates and identifiers still benefit from lexical matching.
Operationally, this means semantic search is less a magic replacement for search and more a retrieval layer that broadens the match space while still working with structured constraints and business rules.
Examples
Internal knowledge lookup: staff ask plain-language questions about leave rules, approval processes or onboarding steps and retrieve the most relevant policy sections.
Support resolution search: agents search past incidents in customer language and still surface the right troubleshooting article or previous case.
Project archive search: a delivery team searches for similar implementations, risks or decisions across historical notes and status reports.
Research and drafting support: a team preparing a proposal or brief can ask for prior examples by theme rather than remembering exact filenames.
Common misunderstandings
"Semantic search means exact keywords no longer matter." They still matter for names, numbers, legal clauses, codes and highly specific product terms.
"Semantic search and RAG are the same." They are related, but not identical. Semantic search is a retrieval method. Retrieval-augmented generation is the broader pattern that uses retrieved material to inform a generated answer.
"If results are semantically similar, they must be correct." Similarity is not proof. Source quality, freshness and permissions still need checking.
"Semantic search should run on whole documents only." Often it works better on smaller chunks, because the returned unit is more targeted and easier to cite or inspect.
Risks and boundaries
The biggest risk is assuming semantic retrieval is enough on its own. In practice, workflows often need hybrid search, metadata filters and permission-aware ranking.
There is also a quality boundary. Dense retrieval can miss a crucial exact-match detail, while approximate search methods can trade a little recall for speed. That is why testing on real questions matters more than generic benchmark optimism.
Finally, semantic search does not solve bad source content. If the underlying policies, notes or records are unclear, outdated or duplicated, the search layer will still struggle to produce dependable results.
What to do next
Take one live search task and create a small evaluation set of real questions people ask. Compare keyword-only, semantic-only and hybrid retrieval, then inspect the top results manually. The right approach is the one that finds the right evidence with the least friction, not the one with the most impressive label.
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FAQs
Is semantic search better than keyword search?
Not in every case. It is better when meaning varies, but keyword search remains strong for exact terms, IDs and structured lookups.
Do I need a vector database for semantic search?
Often you need some form of vector indexing, but that can live in a dedicated vector store or inside a search engine or database that supports vector search.
Can semantic search work across languages?
Yes, if the embedding model and search setup support multilingual retrieval.
Why does hybrid search matter?
Because semantic similarity and exact matching solve different retrieval problems. Combining them often gives more dependable results.
Is semantic search only for AI assistants?
No. It is also useful for human-facing workplace search, recommendation and document discovery.