Evaluating RAG pipelines with Ragas and Openlayer

Shahul Es discusses the challenges of fine-tuning Retrieval-Augmented Generation (RAG) pipelines for production, emphasizing the importance of selecting the right tools and parameters to ensure high-quality output. Using data from Arxiv papers on prompt engineering, the article demonstrates building a RAG pipeline with Ragas and Openlayer, focusing on synthetic test data generation to create diverse, high-quality datasets. The process involves using llama-index for the RAG pipeline and Openlayer for evaluation, employing component-wise testing and metric-driven development to improve system performance systematically. The article highlights the benefits of using Openlayer and Ragas both during development and in production environments to monitor and maintain the quality and performance of RAG systems, with regular testing on production data to ensure timely corrective actions.