Why RAG Pipelines Break in Production (and How I Fix Them)

Retrieval augmented generation demos beautifully and breaks quietly. In a notebook with ten hand-picked documents, RAG looks solved. Point the same pipeline at a real knowledge base of ten thousand me

shreyans-tech.hashnode.dev7 min read

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KN

Kartik N V J K

AI Developer | Making AI reliable, trustworthy & accessible to everyone | Active community contributor

"Retrieve wide, rerank narrow" is the highest-leverage line here, and it matches what fixed most of my retrieval-quality problems for a small latency cost. The failure mode I'd add to your five is silent drift: the index slowly goes stale and nobody notices because nothing errors, the answers just quietly get worse. How are you catching that one, scheduled re-embedding or an eval that watches retrieval quality over time?

Aug 3

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Why RAG Pipelines Break in Production (and How I Fix Them)

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