A Tool-Agnostic Framework for Retrieval-Augmented Generation (RAG) Evaluation
Abstract
Retrieval-Augmented Generation (RAG) has become a cornerstone of modern AI systems, effectively grounding Large Language Models (LLMs) in external, up-to-date knowledge sources. Despite its widespread adoption, the evaluation of RAG systems remains a significant challenge, characterized by a lack of standardized metrics and methodologies. This paper introduces the RAG Quality Evaluation (RAG-QE) Framework, a comprehensive, tool-agnostic methodology for assessing the performance of RAG pipelines. The framework deconstructs the evaluation process into two primary components, the Retriever and the Generator, and proposes a multi-dimensional set of metrics to assess each. Key dimensions include Contextual Precision and Recall for the retriever, and Faithfulness and Answer Relevance for the generator. By providing a structured, reproducible, and vendor-neutral approach, the RAG-QE Framework empowers developers and researchers to benchmark, debug, and systematically improve their RAG systems, thereby fostering more reliable and trustworthy AI applications.
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