Loop Engineering for RAG: The Small Loops Inside Each Step, the Big Loops Across the Pipeline

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Enterprise RAG pipelines have a clean path and a messy reality. The four core components — parser, retriever, generator, and schema validator — handle the clean path. Loop engineering is everything the system does when those components don't succeed on the first try. This article, part of an ongoing Enterprise Document Intelligence series on Towards Data Science, frames loops at two scales: small loops inside individual pipeline steps (retry a retrieval miss, reformat failed JSON) and big loops across the full pipeline (re-run the document flow when early failures cascade). Every loop requires three explicit design decisions: what triggers it, when it terminates, and how it recovers before the next attempt. Without all three, a loop that was meant to recover instead spins. The piece references Boris Cherny's (Anthropic, Claude Code) framing of loop-based prompt engineering, adapting it for enterprise RAG where failures are ordinary rather than exceptional. One-shot pipelines commit to their first try; a loop gives the system a second chance before a bad result reaches the user. For practitioners moving a RAG prototype toward production, this article provides a concrete vocabulary and framework for the reliability layer that sits between raw prompt engineering and full agent design. Recommended reading for anyone building multi-step document intelligence pipelines.