Effective communication with AI models involves distilling insights from real-world user interviews and establishing clear expectations regarding tone and priority, particularly for VIP customers. Providing extensive and detailed guidelines enhances model performance; more information is beneficial. Crucially, requests should specify that reasoning precede answers to avoid mere post hoc justifications. Using structured formats, such as XML or JSON, enhances clarity. Implementing gold standard examples helps refine outputs, allowing for multiple tests based on a set of examples for robust learning. Continuous evaluation of model effectiveness is essential after testing to ensure desired outcomes are being achieved.

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