LLMOps for RAG Systems — Production Checklist

Why the pipeline, not the model, is usually to blame

If your LLM feature starts hallucinating or your inference bill doubles overnight, the model is rarely the root cause. In production RAG systems the usual culprits are missing operational practices: weak retrieval, no reranker, no prompt/version control, poor eval coverage, and blunt cost controls. Treating RAG as “embed → nearest neighbors → prompt” is how teams discover outages, bad answers, and runaway spend.

This article gives a compact, practical LLMOps for RAG systems checklist I use with engineering teams to ship safer, cheaper features in 2–4 sprints.

The 5-step checklist (what to implement and why)

1) Hybrid retrieval (dense + sparse)

LLMOps for RAG Systems — Production Checklist

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