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The chapter explores the challenges and opportunities of collaboration and evaluation in NLP models, emphasizing the significance of prompt engineering and managing prompts for customizing AI models. The speakers also discuss the subjective nature of measuring performance in generative AI and highlight the importance of evaluation to ensure expected model behavior.

Small changes in prompts can create large changes in the output behavior of generative AI models. Add to that the confusion around proper evaluation of LLM applications, and you have a recipe for confusion and frustration. Raza and the Humanloop team have been diving into these problems, and, in this episode, Raza helps us understand how non-technical prompt engineers can productively collaborate with technical software engineers while building AI-driven apps.

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