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Enhancing Data Collection and Model Intelligence
The chapter explores the design process of the ADAPT agent to optimize data collection and improve model intelligence, emphasizing training the model to interpret reward signals effectively. It underlines the importance of reliability in agent tasks and the value of Reinforcement Learning from Human Feedback (RLHF) with real-world data scenarios. The discussion also emphasizes the significance of reliability in AI products for enterprise settings and the challenges of building an adaptable AI stack.