Evaluating RAG with LLM as a Judge Evaluating Large Language Models (LLMs), particularly Retrieval-Augmented Generation (RAG) systems, presents challenges in ensuring outputs are relevant and grounded. The ‘LLM As A Judge’ approach uses one LLM to grade another’s responses, while the RAG Triad framework assesses context relevance, groundedness, and answer relevance. Mistral’s structured outputs offer a practical way to implement these evaluation methods for more reliable AI applications.

• Evaluating LLM systems requires verifying not just coherence but also relevance and adherence to requirements.
• Retrieval-Augmented Generation (RAG) systems enhance LLMs by grounding responses in retrieved information, reducing hallucinations.
• Evaluating RAG systems involves checking the relevance and accuracy of retrieved information in addition to the LLM’s output.
• ‘LLM As A Judge’ uses a separate LLM to grade the performance of a generator LLM at scale.
• The RAG Triad framework evaluates RAG systems based on Context Relevance, Groundedness, and Answer Relevance.
• Mistral’s structured outputs provide a machine-readable format to implement the RAG Triad and ‘LLM As A Judge’ for more reliable evaluation. Continue reading https://foxvector.com/articles/ad629411-8be1-4c21-853e-156afc8f83e8