Navigating AI Model Implementation

This chapter delves into the complexities of experimenting with AI models in-house, examining data handling and its effect on output quality. The discussion covers strategies for enhancing AI interactions, including prompt engineering and retrieval augmented generation, as well as the importance of fine-tuning models to improve relevance and accuracy. Additionally, it emphasizes the significance of establishing clear success criteria and understanding the risks of overtraining in the context of deploying large language models.

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