RAGEvalX: An Extended Framework for Measuring Core Accuracy, Context Integrity, Robustness, and Practical Statistics in RAG Pipelines

Retrieval-Augmented Generation (RAG) has emerged as a cornerstone for building context-aware and factual Large Language Model (LLM) applications. However, evaluating the performance of these complex pipelines remains a significant challenge. Existing evaluation frameworks often focus on a narrow set of metrics, failing to provide a holistic view of a system's accuracy, reliability, and practical usability. This paper introduces RAGEvalX, an extended, multi-faceted evaluation framework designed to address this gap. RAGEvalX systematically measures four crucial dimensions: (1) Core RAG Accuracy, including faithfulness and relevancy; (2) Context Integrity, assessing the quality and utilization of retrieved information; (3) Robustness against common input perturbations; and (4) Practical Statistics for operational monitoring. We provide a detailed methodology for implementing the framework, complete with code snippets and guidance on LLM selection for evaluation tasks. Through case studies in healthcare, finance, and legal sectors, we demonstrate how RAGEvalX provides actionable insights for optimizing RAG pipelines. Our framework offers a standardized, comprehensive, and implementable approach to ensure RAG systems are not only accurate but also reliable and ready for real-world deployment.