Evaluating RAG with LLM as a Judge | Mistral AI

What this post added

This post introduces a method for evaluating RAG systems using an LLM as a judge, specifically leveraging the RAG Triad (Context Relevance, Groundedness, Answer Relevance) and Mistral's structured outputs feature. It provides a Python code example using Pydantic models to define the evaluation schema for ContextRelevance, AnswerRelevance, and Groundedness, demonstrating how to structure LLM outputs for machine-readable evaluation. This enhances the evaluation component of the Search Toolkit by providing a concrete approach to assessing RAG pipeline performance.