Semantic search finds results based on meaning and intent rather than exact keyword matches. It converts queries and documents into embeddings and retrieves the items closest in meaning, so a search for "ways to cut cloud spend" can surface a doc titled "reducing infrastructure costs" even with no shared words.
Key takeaways
- Semantic search matches meaning, not exact keywords — handling synonyms, phrasing, and intent.
- It works by embedding the query and documents and finding the nearest vectors.
- Hybrid search combines semantic and keyword methods for the best of both.
- It is the retrieval layer behind RAG and a major upgrade for enterprise knowledge discovery.
Semantic search, defined
Semantic search retrieves information by understanding what a query means rather than matching the exact words it contains. Traditional keyword search looks for documents that literally contain your search terms; semantic search looks for documents that are about the same thing, even when the wording differs entirely.
This closes the long-standing gap between how people phrase questions and how documents are written. A user searching "why is my deployment failing" can find a runbook titled "troubleshooting release errors" because the two share meaning, not vocabulary.
How semantic search works
Under the hood, semantic search relies on embeddings. Documents are embedded ahead of time and stored in a vector database. When a query arrives, it is embedded too, and the system returns the documents whose vectors are closest to the query vector — the most semantically similar results.
Production systems add re-ranking (a second pass that orders the top candidates more precisely) and metadata filtering (restrict by source, date, or permissions). The result is fast, relevance-ranked retrieval that understands intent.
Hybrid search: semantic + keyword
Semantic search is not always strictly better. Keyword search still wins for exact identifiers — part numbers, error codes, names — where literal matching is precisely what you want. Hybrid search runs both and blends the scores, capturing semantic relevance and exact precision together.
Most strong enterprise retrieval systems are hybrid for this reason. They lean on semantic similarity for natural-language questions while preserving keyword accuracy for the cases where exact terms matter.
Semantic search in the enterprise
Semantic search transforms enterprise knowledge discovery — internal wikis, policy libraries, support content, and code become findable by meaning. It is also the retrieval engine inside RAG, so the quality of an AI assistant's answers depends directly on the quality of its semantic search.
The governance requirement is the same as for any retrieval: results must respect permissions, and the index should stay on controlled infrastructure. Otherwise semantic search can surface content to people who should not see it — a relevance win that becomes a compliance failure.
Keyword Search vs Semantic Search
Hybrid systems combine both — exact precision plus meaning-based recall.
| Dimension | Keyword Search | Semantic Search |
|---|---|---|
| Matches on | Exact terms | Meaning and intent |
| Synonyms | Missed unless added | Handled naturally |
| Natural-language queries | Often weak | Strong |
| Exact IDs / codes | Strong | Sometimes weaker |
| Technology | Inverted index | Embeddings + vector search |
| Best approach | Combine the two | Combine the two (hybrid) |
From concept to a governed, on-premise reality
VDF AI provides semantic search over your private content with permission-aware results and on-premise indexing, so meaning-based discovery never compromises access control or data residency.
It is the retrieval foundation for VDF AI Chat and agent workflows on VDF AI Networks — typically hybrid, blending semantic relevance with keyword precision for enterprise-grade accuracy.
Frequently asked questions
What is semantic search?
Search that finds results by meaning and intent rather than exact keyword matches. It uses embeddings and vector search to return documents that are about the same thing as the query, even if they share no words.
How is semantic search different from keyword search?
Keyword search matches literal terms; semantic search matches meaning. Semantic search handles synonyms and natural-language questions well, while keyword search is better for exact identifiers. Hybrid search combines both.
How does semantic search work?
Documents and the query are converted into embeddings, and the system returns the documents whose vectors are closest to the query vector — often with re-ranking and metadata filtering for precision.
What is hybrid search?
A method that runs both semantic and keyword search and blends their scores, capturing meaning-based relevance and exact-term precision together. Most strong enterprise retrieval systems are hybrid.
Is semantic search the same as RAG?
No, but related. Semantic search is the retrieval step; RAG adds a generation step that uses the retrieved results to produce a grounded answer. RAG depends on good semantic search.
Is semantic search secure for internal data?
It is when results are permission-aware and the index runs on controlled infrastructure. Without those controls, semantic search can surface sensitive content to unauthorized users.
Put these concepts to work on infrastructure you control.
VDF AI runs governed agents, private retrieval, and model routing inside your own cloud, data center, or air-gapped network. Book a walkthrough mapped to your stack.