Valutare RAG con un LLM come giudice

Evaluating Retrieval-Augmented Generation (RAG) systems presents a unique challenge, as it requires assessing both the final answer and the underlying information used to create it. A powerful technique for this is the "LLM as Judge" approach, where one large language model is tasked with assessing another's output against predefined criteria. The RAG Triad offers a comprehensive framework for this evaluation, focusing on three core metrics: the relevance of the retrieved context, the answer's factual grounding, and its relevance to the user's original query. This method ensures that AI-generated responses are not only coherent but also accurate, reliable, and contextually appropriate.