A System for Comprehensive Assessment of RAG Frameworks
This provides a scalable and adaptable solution for researchers and industry professionals evaluating RAG applications, though it is incremental as it builds on existing evaluation needs without fundamentally changing the RAG paradigm.
The authors tackled the lack of holistic black-box evaluation frameworks for Retrieval Augmented Generation (RAG) systems in real-world deployments by introducing SCARF, a modular and flexible system that benchmarks RAG applications through automated testing and detailed performance reports.
Retrieval Augmented Generation (RAG) has emerged as a standard paradigm for enhancing the factual accuracy and contextual relevance of Large Language Models (LLMs) by integrating retrieval mechanisms. However, existing evaluation frameworks fail to provide a holistic black-box approach to assessing RAG systems, especially in real-world deployment scenarios. To address this gap, we introduce SCARF (System for Comprehensive Assessment of RAG Frameworks), a modular and flexible evaluation framework designed to benchmark deployed RAG applications systematically. SCARF provides an end-to-end, black-box evaluation methodology, enabling a limited-effort comparison across diverse RAG frameworks. Our framework supports multiple deployment configurations and facilitates automated testing across vector databases and LLM serving strategies, producing a detailed performance report. Moreover, SCARF integrates practical considerations such as response coherence, providing a scalable and adaptable solution for researchers and industry professionals evaluating RAG applications. Using the REST APIs interface, we demonstrate how SCARF can be applied to real-world scenarios, showcasing its flexibility in assessing different RAG frameworks and configurations. SCARF is available at GitHub repository.