RAGXplain: From Explainable Evaluation to Actionable Guidance of RAG Pipelines

Abstract

Retrieval-Augmented Generation (RAG) systems couple large language models with external knowledge, yet most evaluation methods report aggregate scores that reveal whether a pipeline underperforms but not where or why. We introduce RAGXplain, an evaluation framework that translates performance metrics into actionable guidance. RAGXplain structures evaluation around a 'Metric Diamond' connecting user input, retrieved context, generated answer, and (when available) ground truth via six diagnostic dimensions. It uses LLM reasoning to produce natural-language failure-mode explanations and prioritized interventions. Across five QA benchmarks, applying RAGXplain's recommendations in a single human-guided pass consistently improves RAG pipeline performance across multiple metrics. We release RAGXplain as open source to support reproducibility and community adoption.