Your RAG system is hallucinating even though the correct context was retrieved. How do you debug it?

The short answer

Check that the retrieved chunk actually contains the answer and survived the context window without truncation, then inspect prompt construction and instructions telling the model to answer only from context. Add grounding and citation requirements, lower temperature, and use a faithfulness metric or judge to verify the answer is entailed by the retrieved text, also checking for conflicting context or parametric-knowledge override.

How to think about it

Check that the retrieved chunk actually contains the answer and survived the context window without truncation, then inspect prompt construction and instructions telling the model to answer only from context. Add grounding and citation requirements, lower temperature, and use a faithfulness metric or judge to verify the answer is entailed by the retrieved text, also checking for conflicting context or parametric-knowledge override.

Learn it properly Hallucination & grounding

Keep practising

What is Retrieval-Augmented Generation (RAG) and how does a basic RAG pipeline work? What is Retrieval-Augmented Generation (RAG) and why is it used? When should you use RAG vs fine-tuning vs a long-context model? How do you evaluate the quality of an LLM or RAG system? What are chunking strategies in RAG, and how do you choose chunk size?

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Advanced RAG RAG evaluations RAG basics

RAG Hallucination Chain-of-Thought Faithfulness