Exploring Innovations in Synthetic Data and LLM Applications

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This chapter explores the role of synthetic data in enhancing large language model (LLM) applications, emphasizing advancements in model performance and relevant tool use cases. It also discusses the need for improved evaluation standards in AI development and expresses optimism about future collaborations in the community.

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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