Synthetic data for RAG evaluation: Why your RAG system needs better testing

This blog post discusses the challenges of evaluating Retrieval-Augmented Generation (RAG) systems and proposes the use of synthetic data generation for enhanced testing. It identifies issues such as retrieval entanglement, lack of ground truth, and domain-specific blind spots that complicate evaluation. It suggests using the SDG Hub, an open-source Python framework, to create high-quality evaluation datasets that ensure reliable assessment of RAG system performance, enabling systematic improvement and debugging processes.