Why Your RAG Is Missing Half the Results — Hybrid Search Explained #AIEngineering

Pure vector search finds what is similar. It misses exact terms, IDs, and technical strings. BM25 finds exact matches. It misses synonyms and semantic meaning. Run either one alone - recall at 5: 40 percent.

Hybrid Search runs both in parallel and merges with Reciprocal Rank Fusion. 15 lines. Zero retraining. Recall jumps to 80 percent on the same index.

What you learn:
- Why BM25 and dense vectors each fail alone on real queries
- Reciprocal Rank Fusion: score equals 1 divided by k plus rank
- Why position in the ranking beats raw similarity scores
- Full implementation in 15 lines, plug into any vector store
- Chroma, Pinecone, Qdrant, Weaviate - zero infrastructure changes

AI Engineering Patterns Series - one production pattern per week.

#AIEngineering #RAG #HybridSearch #BM25 #Python #Shorts

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