Prompt engineering is the process of writing effective instructions for a GPT model, such that it generates content that meets desired outcomes. And because the content generated from a model is non-deterministic, that is, even for the same input, the model can generate different responses on different runs, and the rich datasets combine art and science, prompting can be particularly useful in creative fields such as design. However, there’s more to effective human-AI collaboration than a perfect prompt. And so, conversational prompting is a technique that involves interacting with AI systems like ChatGPT in a human-like conversation. That is, users describe in everyday terms what they want ChatGPT to do rather than trying to craft complex prompts. In this, users engage in back-and-forth interactions to refine the results, provide additional context, and answer the ChatGPT's questions. That is, the user guides the process while letting ChatGPT handle the specifics of generating appropriate prompts and responses. Problem solving, then, it is argued, is enhanced by balancing human ingenuity and machine intelligence. However, conversational prompting, as a feed-back model, carries risks too. That is, the interaction may contain misinformation, biases or illusions begging the question: Is the output reliable and trustworthy? Selected sources: https://platform.openai.com/docs/guides/prompt-engineering https://promptengineering.org/conversational-prompting-in-generative-ai/
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