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Why RAG fails in production — and where
When RAG breaks, the model usually isn't the problem — retrieval is, about 73% of the time. Here's the real failure map and what to fix first.
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#retrieval
4 posts.
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Naive retrieve-then-generate isn't enough for production
The 5-line RAG baseline is great for a demo and fragile in production. Here's where it breaks and the upgrade path that fixes it.
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Hybrid search for RAG: combining BM25 and dense vectors with RRF
Why you can't average keyword and vector scores — and how Reciprocal Rank Fusion combines them for an 8–15% accuracy gain.
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How to choose chunk size and overlap for RAG
Start at 512 tokens with light overlap, then tune by query type. A practical, benchmark-backed guide to chunk size for retrieval.
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