Evaluating RAG with LLM as a Judge

Evaluating the performance of Large Language Models (LLMs), especially Retrieval-Augmented Generation (RAG) systems, is a complex task. One emerging solution is the "LLM As A Judge" method, where an LLM is used to grade the output of another model against specific criteria. The RAG Triad is a popular framework for this, focusing on three key metrics: the relevance of the retrieved context, the groundedness of the answer in that context, and the relevance of the final answer to the original query. This approach provides a comprehensive view of system performance, helping to reduce hallucinations and ensure responses are accurate and reliable. Tools like structured outputs can be used to implement this evaluation by enforcing a consistent, machine-readable format for the judging LLM's assessment.