Debugging RAG: A step-by-step guide to tracing evidence failures

This article provides a structured approach to debugging issues in retrieval-augmented generation (RAG) systems, focusing on tracing the evidence flow. It outlines a repeatable investigation process to pinpoint failures in fact retrieval, search execution, context assembly, or unsupported generation. The guide emphasizes starting with a reproducible case, documenting expected evidence, and comparing it against observed output and cited sources, ultimately suggesting a method to change one variable at a time and maintain a failure record for iterative improvement. AI

IMPACT Provides a structured methodology for developers to improve the reliability and accuracy of RAG systems.

RANK_REASON Article provides a practical guide and workflow for debugging a specific AI system architecture (RAG).

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