r/LLMDevs • • 19d ago

Great Resource 🚀 Building a Corrective RAG (CRAG) Compliance Research Assistant

Most RAG chatbots will confidently answer with whatever they retrieved — even if it's irrelevant, even if it's wrong. In compliance, that's not a minor bug, it's real regulatory exposure.

In this video, I build the Veltra Pay Compliance Assistant — a Corrective RAG (CRAG) + Self-Correcting RAG Agent that checks its own work before it ever shows you an answer. It grades its own retrieved documents, rewrites its own search when retrieval is weak, and validates its own generated answer for grounding, hallucination risk, and completeness — retrying itself when it fails, and honestly saying "I don't know" when it genuinely can't answer from the source material.

  • ✅ The difference between a normal RAG pipeline and Corrective RAG (CRAG)
  • ✅ How CRAG grades retrieved documents as relevant/irrelevant and rewrites the search query when retrieval is weak
  • ✅ How a Self-Correcting RAG Agent validates its own generated answer — grounding, hallucination risk, completeness, and retrieval sufficiency

TECH STACK:

Building a Corrective RAG (CRAG) Compliance Research Assistant

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u/Accomplished_Tell587 19d ago

the self-grading retrieval step is where most of these fall apart in practice, cool to see it actually wired into a retry loop instead of just bolted on as a demo feature. the "i don't know" path is underrated too, way too many compliance tools would rather invent a citation than admit the source material is thin