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The PROMPT Framework for prompt engineering can enhance interactions with AI models like ChatGPT. Use the framework to optimize prompts to maximize utility and comprehension. Thoughtful prompting is key to developing critical thinking and information literacy in the age of generative AI.
The advent of Generative AI and large language models like ChatGPT has opened up new frontiers for interacting with AI. However, these models have limitations that users should understand. They are trained on vast datasets, not real-world experience, and can hallucinate answers that seem plausible but are inaccurate. Their knowledge also cuts off when the model is trained, so they lack up-to-date information.
This is why practices like asking for explanations, sources, and logical reasoning are so important. Models can appear confidently convincing even when wrong, so requiring evidence and walking through step-by-step logic helps safeguard against false assurance and exposes gaps. Asking conversational, multi-step questions also allows probing a topic from multiple angles, surfacing inconsistencies an AI might gloss over with sweeping generalizations.
Understanding these intrinsic limitations informs how to engineer prompts for more transparent, robust conversations. The PROMPT Framework below builds on this foundation, equipping users to maximize value from AI interactions while minimizing risks. The key is keeping the human firmly in the loop by prompting not just for answers, but for ethical, explainable reasoning that can be critically evaluated.
Step 1: Ask Explainable Prompts
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