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This chapter delves into the creation of the open-source library RAGAS, specifically aimed at improving the evaluation of applications using natural language models. The discussion covers the challenges of manual evaluations, the benefits of automation, and the creators' machine learning backgrounds that shaped RAGAS and its metrics.

How do you systematically measure, optimize, and improve the performance of LLM applications (like those powered by RAG or tool use)? Ragas is an open source effort that has been trying to answer this question comprehensively, and they are promoting a “Metrics Driven Development” approach. Shahul from Ragas joins us to discuss Ragas in this episode, and we dig into specific metrics, the difference between benchmarking models and evaluating LLM apps, generating synthetic test data and more.

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