The Search Problem: Why Keyword Search Fails for Semantic Queries
~11 min read
Keyword search only matches literal words, so it misses queries that mean the same thing but use different words. Vector search fixes this by matching MEANING, not spelling.
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Key points
- •Keyword search matches literal words, so it misses relevant results that use different words for the same meaning (vocabulary mismatch)
- •It also has the opposite problem: matching shared words that mean something entirely different (e.g. 'apple' the fruit vs the company)
- •Vector/semantic search embeds the query and documents into the same space, then finds documents whose MEANING is closest, regardless of literal wording
- •This is exactly why RAG relies on vector search — an LLM needs genuinely relevant context, and relevance is a meaning problem, not a spelling problem
- •Production systems often combine both approaches ('hybrid search'), since keyword search still excels at exact matches like IDs or names