The RAG reality check: New open-source framework lets enterprises scientifically measure AI performance

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Enterprises are spending time and money building out retrieval-augmented generation (RAG) systems. The goal is to have an accurate enterprise AI system, but are those systems actually working? The inability to objectively measure whether RAG systems are actually working is a critical blind spot. One potential solution to that challenge is launching today with the debut of the Open RAG Eval open-source framework. The new framework was developed by enterprise RAG platform provider Vectara working together with Professor Jimmy Lin and his research team at the University of Waterloo. Open RAG Eval transforms the currently subjective ‘this looks better than that’ comparison approach into a rigorous, reproducible evaluation methodology that can measure retrieval accuracy, generation quality and hallucination rates across enterprise RAG deployments. The framework ... Read full article.