RAGe Framework Benchmarks Retrieval-Augmented Generation Systems

A new modular framework called RAGe (Retrieval-Augmented Generation Evaluation) has been proposed to benchmark and guide the efficient development of RAG applications. The framework addresses challenges such as high computational demands, outdated knowledge bases, and manual selection of pipeline components. RAGe focuses on resource telemetry and component recommendation, suggesting optimal components for domain-specific datasets. It leverages core LLM techniques including document chunking, vector databases, embedding models, and retrievers to evaluate trade-offs among accuracy, efficiency, and scalability. By correlating retrieval and generation quality with hardware constraints, RAGe helps researchers identify effective, domain-specific RAG setups. The framework is detailed in arXiv paper 2605.27445.