Assessing RAG: A Comprehensive Review of Evaluation Frameworks

Retrieval-Augmented Generation (RAG) pipelines, which combine retrieval and generative AI components, are becoming crucial for accurate context-aware text generation in a variety of applications. However, this remains challenging for RAG systems to evaluate due to their complexity and the need to evaluate retrieval and generation components. This survey is conducted to fill the gap in systematic evaluation methodologies by examining existing RAG evaluation frameworks and providing practical advice to improve performance. We compare and evaluate according to metrics such as precision, faithfulness, and adaptability. Our analysis shows that open-source tools focus on transparency and customization but demand some technical knowledge, while the proprietary options focus on integration ease at the cost of flexibility. Major findings show that the granularity of metrics is related to the computational cost and proximity to the domain-specific needs. This work highlights the importance of rigorous evaluation frameworks for RAG systems to achieve practical reliability in real-world applications, thus closing the gap between theoretical benchmarks and real-world deployment.