Evaluating Medical RAG with NVIDIA AI Endpoints and Ragas

Retrieval-augmented generation (RAG) is revolutionizing the medical field by combining large language models with external knowledge retrieval to provide accurate and contextually relevant information. This hybrid approach is particularly beneficial in drug discovery and clinical trial screening. However, evaluating RAG systems for medical applications poses unique challenges, including scalability, lack of benchmarks, and the need for domain-specific metrics. The post discusses using LangChain NVIDIA AI endpoints and the Ragas evaluation framework to address these challenges, with a detailed tutorial on setting up and evaluating medical RAG systems using a synthetic dataset.