RAGXplain: From Explainable Evaluation to Actionable Guidance of RAG Pipelines
This addresses the challenge of user trust and practical optimization for developers and users of RAG systems, though it is incremental as it builds on existing evaluation methods.
The paper tackles the problem of limited actionable guidance in evaluating Retrieval-Augmented Generation (RAG) systems by introducing RAGXplain, a framework that quantifies performance and provides clear insights and recommendations, resulting in measurable performance improvements on public datasets.
Retrieval-Augmented Generation (RAG) systems show promise by coupling large language models with external knowledge, yet traditional RAG evaluation methods primarily report quantitative scores while offering limited actionable guidance for refining these complex pipelines. In this paper, we introduce RAGXplain, an evaluation framework that quantifies RAG performance and translates these assessments into clear insights that clarify the workings of its complex, multi-stage pipeline and offer actionable recommendations. Using LLM reasoning, RAGXplain converts raw scores into coherent narratives identifying performance gaps and suggesting targeted improvements. By providing transparent explanations for AI decision-making, our framework fosters user trust-a key challenge in AI adoption. Our LLM-based metric assessments show strong alignment with human judgments, and experiments on public question-answering datasets confirm that applying RAGXplain's actionable recommendations measurably improves system performance. RAGXplain thus bridges quantitative evaluation and practical optimization, empowering users to understand, trust, and enhance their AI systems.