Evaluating Retrieval-Augmented Generation Systems: Challenges and Practices

Retrieval-augmented generation (RAG) systems integrate external information sources with advanced large language models (LLMs), promising more factual and context-aware outputs. However, assessing their performance remains a significant challenge—it involves assessing not only generated text quality but also the relevance and correctness of retrieved information. Recent developments have introduced new frameworks and metrics, but the field has not converged on widely accepted standards for robust evaluation.

In this talk, we explore current approaches to RAG evaluation and discuss key open questions from ongoing research in this area. Our goal is to encourage more systematic approaches for measuring and comparing RAG performance in a continually advancing field.