# Semantic search

> Semantic search finds results by meaning instead of exact words, so a query can match relevant content even when they share no vocabulary.

- Page: https://methodcrew.com/glossary/semantic-search
- Last updated: 2026-10-28
- Publisher: [The Method Crew](https://methodcrew.com/)
- Also known as: vector search, neural search

## How it works

Classic keyword search, the kind built into Elasticsearch or a website search box, scores documents by how often the query's words appear in them. Semantic search converts the query and every document passage into [embeddings](https://methodcrew.com/glossary/embeddings), then returns the passages whose vectors sit closest. A search for "laptop won't turn on" can find an article titled "Troubleshooting power issues" even though no words match. Sentence embedding models such as Sentence-BERT ([Reimers and Gurevych, 2019](https://arxiv.org/abs/1908.10084)) made this fast enough to run over large collections.

Results can then pass through a reranker: a second model that reads the query and each candidate together and scores relevance more precisely. Rerankers are slower, so they usually see only the top 20 to 100 results.

## How businesses use it

- Help center and intranet search, where staff and customers describe problems in their own words
- The retrieval step of [RAG](https://methodcrew.com/glossary/retrieval-augmented-generation) assistants
- Finding similar past cases: support tickets, claims, contracts, project files
- Matching job descriptions to résumés, or products to customer requests

It works best on prose, such as articles, emails, notes and call transcripts. Our [RAG knowledge systems](https://methodcrew.com/services/rag-knowledge-systems) work uses it as one half of the retrieval layer.

## Where it goes wrong

Semantic search is poor at exact matches. Search for invoice INV-20817 or part 4410-B and it returns things that look similar, which is the wrong answer. Queries of 1 or 2 words also give it very little meaning to work with.

It also returns something every time, even when nothing relevant exists. An assistant built on it needs a relevance threshold, below which it says it found nothing instead of answering from a weak match.

The fix for exact terms is [hybrid search](https://methodcrew.com/glossary/hybrid-search), which runs keyword and semantic search together and merges the results. Before buying a new search product, test candidate setups on 50 real queries from your own search logs and read what comes back.

## Example (hypothetical)

Hypothetical: a 60-person IT services company has 12,000 resolved tickets. Semantic search over the resolution notes lets a new technician describe the symptom in plain words and see the 5 most similar past tickets with their fixes.

## Related terms

- [Embeddings](https://methodcrew.com/glossary/embeddings): Embeddings are lists of numbers that capture the meaning of text or images, so software can measure how closely related one item is to another.
- [Hybrid search](https://methodcrew.com/glossary/hybrid-search): Hybrid search runs keyword search and semantic search on the same query and merges the results, so it catches exact terms and paraphrased meaning together.
- [Vector database](https://methodcrew.com/glossary/vector-database): A vector database stores embeddings, the numeric fingerprints of text or images, and quickly finds the stored items whose meaning is closest to a query.
- [Retrieval-augmented generation (RAG)](https://methodcrew.com/glossary/retrieval-augmented-generation): Retrieval-augmented generation is a method where a system first searches your own documents for relevant passages, then gives them to a language model to answer from.

## Services where this matters

- [RAG and knowledge systems](https://methodcrew.com/services/rag-knowledge-systems): Your documents turned into an assistant that cites its sources. $15,000 to $75,000 per project.
- [Custom AI agents and copilots](https://methodcrew.com/services/custom-ai-agents): Agents that look things up in your CRM or helpdesk and take the next step, with approvals. $15,000 to $90,000 per project.

Full glossary: https://methodcrew.com/glossary