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Retrieval-Augmented Generation lets you ground answers in your own ops manuals. Most RAG systems fail not at generation but at retrieval — here's how to fix that.
When a RAG system gives a wrong answer, the LLM is rarely the culprit. The retrieval step pulled the wrong passages, and the LLM faithfully summarized them. Improving generation prompts won't fix this. Improving retrieval will.
Embedding similarity gets you 'topically close.' That's not the same as 'answers the question.' A reranker — even a small one — re-scores the top-50 retrieved chunks against the actual query. Adding a reranker is often a 20-30 point retrieval-quality jump.
| Symptom | Likely cause | Fix |
|---|---|---|
| Right topic, wrong specifics | Chunks too small, missing context | Bigger chunks or parent-doc lookup |
| Hallucinated steps | Retrieval missed the actual procedure | Reranker, better embeddings |
| Outdated answer | Stale chunks not re-indexed | Scheduled re-embedding job |
| Confidently wrong | Generation prompt not strict enough | Force 'answer only from passages' grounding |
The big idea: RAG quality is retrieval quality. Build the eval set, then tune retrieval, then worry about the LLM.
15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-operations-rag-for-ops-manuals-adults
What is the core idea behind "RAG For Ops Manuals: Retrieval That Actually Retrieves"?
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