Vector Search vs Keyword Search: Which One Should Your AI Product Use?
Search quality shapes AI product quality. If your retrieval layer finds the wrong information, even the best LLM will struggle.
Two common approaches are keyword search and vector search.
Keyword search
Keyword search works well when users know exact terms, product names, error codes, or IDs.
Strengths:
- precise exact matching
- fast and mature
- easy to debug
- strong filters
- good for names and codes
Weaknesses:
- misses synonyms
- struggles with vague queries
- depends on wording
Vector search
Vector search finds semantically similar content. It can match meaning even when wording differs.
Strengths:
- handles natural language
- finds related concepts
- good for RAG
- supports recommendations
- useful for messy user queries
Weaknesses:
- can retrieve plausible but wrong content
- harder to debug
- requires embeddings
- may need reranking
Hybrid search
Many production systems use both. Hybrid search combines keyword precision with vector recall.
This is useful for:
- technical documentation
- support search
- legal documents
- product catalogs
- enterprise knowledge bases
Which should you choose?
Use keyword search when exact terms matter. Use vector search when meaning matters. Use hybrid search when both matter.
For RAG, hybrid search plus reranking is often stronger than vector search alone.
Final thoughts
Vector search is powerful, but it does not replace keyword search everywhere. The best AI products choose retrieval methods based on user intent, data type, and quality requirements.