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RAGXplain: From Explainable Evaluation to Actionable Guidance of RAG Pipelines

Dvir Cohen, Lin Burg, Gilad Barkan

TL;DR

RAGXplain tackles the gap between numeric evaluation and actionable guidance for Retrieval-Augmented Generation by using an LLM-driven Metric Diamond to provide explainable, pipeline-level diagnostics. It validates alignment with human judgments and demonstrates that following its explanations yields measurable improvements on public QA benchmarks, enhancing transparency and trust for diverse users. The framework is modular and adaptable to different LLMs and datasets, bridging evaluation and practical optimization of complex RAG pipelines. By integrating quantitative metrics with natural language explanations and targeted recommendations, RAGXplain offers a concrete path toward more trustworthy and effective RAG systems.

Abstract

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.

RAGXplain: From Explainable Evaluation to Actionable Guidance of RAG Pipelines

TL;DR

RAGXplain tackles the gap between numeric evaluation and actionable guidance for Retrieval-Augmented Generation by using an LLM-driven Metric Diamond to provide explainable, pipeline-level diagnostics. It validates alignment with human judgments and demonstrates that following its explanations yields measurable improvements on public QA benchmarks, enhancing transparency and trust for diverse users. The framework is modular and adaptable to different LLMs and datasets, bridging evaluation and practical optimization of complex RAG pipelines. By integrating quantitative metrics with natural language explanations and targeted recommendations, RAGXplain offers a concrete path toward more trustworthy and effective RAG systems.

Abstract

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.
Paper Structure (69 sections, 2 figures, 3 tables, 1 algorithm)

This paper contains 69 sections, 2 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. RAG Evaluation Frameworks
  4. Explainable AI Metrics
  5. Hybrid Approaches and RAGXplain's Position
  6. RAGXplain Framework
  7. Metrics and the "Metric Diamond"
  8. Human Labeling for Metric Validation
  9. Metric Calculation
  10. Stage 1: Score and Explanation Generation.
  11. Stage 2: Aggregation.
  12. Insight Generation
  13. Processing Details:
  14. Reasoning Process:
  15. Action Item Recommendation
  16. ...and 54 more sections

Figures (2)

  • Figure 1: RAGXplain's evaluation pipeline. The system processes input through three main stages: (1) Metric calculation using LLM-as-judge approach, (2) Insight generation synthesizing patterns across metrics, and (3) Action item recommendation for improvement.
  • Figure 2: RAGXplain's "metric diamond". Each edge represents a relationship between two components of the RAG pipeline, measured by a dedicated metric.