Evaluating RAG Systems: The Metrics That Actually Matter

Author(s): MahendraMedapati

Originally published on Towards AI.

How to measure success and systematically improve your RAG system’s performance

You’ve built your first RAG system, it’s retrieving documents and generating answers… but how do you know if it’s actually working well? How do you measure success? And most importantly, how do you make it better?

The article outlines the importance of evaluating Retrieval-Augmented Generation (RAG) systems, emphasizing that evaluation is essential to discern whether these systems are performing optimally. It details the two-part evaluation challenge — dividing it into retrieval and generation evaluations — and highlights key metrics for assessing both components, including precision, recall, and faithfulness. The piece culminates in a comprehensive framework for systematic evaluation, improvement techniques, and suggestions for creating effective evaluation datasets, all aimed at enhancing the reliability and performance of RAG systems.

Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI