RAG Evaluation Gets a Boost: Exploring RAGAS for Enhanced Accuracy

This article explores the use of RAGAS for evaluating the accuracy of Retrieval-Augmented Generation (RAG) systems, specifically within the domain of sales knowledge. The findings provide valuable insights into the challenges and considerations when aiming to replace human evaluation with an AI-driven approach, highlighting the importance of context and domain expertise in accurate RAG performance.

• •RAGAS, a standard framework for RAG evaluation, was tested to replace human evaluation.
• •The study found that in scenarios without a single correct answer, RAGAS struggled to fully replace human evaluators.
• •The discrepancy in evaluation stemmed from differences in context understanding and evaluation criteria between humans and AI.