#Semantic Search

Articles about Semantic Search — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with semantic search. ← All posts

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Pratik Dhanave · ·6 min read

Semantic and Hybrid Search — Matching by Meaning

Keyword search has one incurable weakness: it matches words, not meaning, so it misses relevant results that use different vocabulary than the query. Semantic search fixes this by representing queries and documents as vectors of meaning and matching by proximity in that space — the technology behind modern RAG. But the surprising lesson of production search is that semantic didn't replace keyword; the best systems combine them. This post explains vector search and why hybrid retrieval wins.

Keyword search matches words, not meaning. Semantic search embeds queries and documents into a vector space and matches by proximity — the engine of RAG — but it's weak at exact matches. Why keyword and semantic have opposite strengths, and why hybrid retrieval (not replacement) is the modern default.

#Information Retrieval#Semantic Search#Hybrid Search#RAG#Search

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.