Retrieval & grounding
Semantic Search Explained
Search that understands intent, not just matching words — and how to build it.
Semantic search finds results based on meaning and intent rather than exact word overlap. A search for "how do I stop my subscription" can correctly surface a help article titled "cancelling recurring billing," which keyword search would likely miss entirely.
This capability, powered by embeddings, is now a standard expectation in modern search experiences, from customer support to internal knowledge bases.
Key takeaways
What makes search "semantic"
A search engine becomes semantic when it converts both the query and the searchable content into embeddings, then ranks results by how close their meanings are, instead of counting matching words.
This is why semantic search handles synonyms, rephrasing, and natural language questions far better than traditional keyword-based systems.
Where keyword search still wins
Exact terms, product codes, names, and acronyms are sometimes better served by keyword matching, because embeddings can occasionally miss a specific, unusual term that a keyword index would catch immediately.
This is why most production-grade search systems combine both — hybrid search — rather than choosing one exclusively.
Building a basic semantic search feature
At a minimum, you need an embedding model to convert content and queries into vectors, a vector database to store and search those vectors, and a ranking step to present the most relevant results to the user.
Adding a reranking step and combining it with keyword search are the two highest-impact upgrades once the basic system is working.
Put this into practice