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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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RAG in Production
Reliability, failure modes, and the architecture that keeps retrieval-augmented generation working with real users.
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Why I don't use LangChain in production RAG
Frameworks are great for a demo. In production, the abstraction you can't see into costs more than it saves.
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When NOT to use RAG (RAG vs fine-tuning vs prompting)
RAG became the default reflex for every LLM problem. Often it's the wrong tool. A straight decision framework for RAG vs fine-tuning vs just prompting.
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A reference architecture for production RAG
The stages every production RAG system needs — indexing, query-time retrieval, and always-on evaluation — and how to order them.
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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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The single most useful log line in a RAG system
If you log one thing in your RAG pipeline, log the final assembled prompt. It's the fastest path from 'bad answer' to root cause.
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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 reduce RAG hallucinations: a practical checklist
Most RAG hallucinations are retrieval bugs in disguise. A concrete checklist to cut them — grounding, retrieval quality, and faithfulness checks.
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How to debug a RAG pipeline that returns wrong answers
A step-by-step debug order for bad RAG answers — start with the assembled prompt, not the model. Most bugs are retrieval, not generation.
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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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RAG chunking strategies: a practical reference
The common chunking approaches for retrieval, when each one helps, and the tradeoffs — a quick reference you can come back to.
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