As we navigate the rapidly evolving landscape of AI, I'd like to pose a question that has been on my mind lately: Can we develop a prompt engineering framework that not only optimizes the input-output mapping of an AI model but also takes into account the inherent biases and context of the human evaluator, thereby enabling the model to learn from and respond to the nuances of human feedback in a way that simulates a genuine conversation?
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