Evaluating RAG with LLM as a Judge

Evaluating Retrieval-Augmented Generation (RAG) systems is challenging because it requires assessing both the retrieved context and the final generated answer. The "LLM as a Judge" technique addresses this by using one large language model to grade the output of another at scale. A popular framework for this is the RAG Triad, which evaluates context relevance, groundedness, and answer relevance to provide a holistic view of performance. Mistral's models can be used as effective judges, particularly when combined with their structured outputs feature to ensure the evaluation is returned in a consistent, machine-readable format.