Important How to use the ChatGPT Playpen forum room

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Presently, participation in the ChatGPT Playpen forum room is limited to the moderating team and PM Bug members that have an active membership upgrade. ...

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Traditional software responds predictably to instructions. “Generative” artificial-intelligence (AI) models, such as that used by ChatGPT, are different: they respond to requests written in everyday language, and can produce surprising results. On the face of it, writing effective prompts for AI is much simpler than, for example, mastering a programming language. But as AI models have become more capable, making the most of the algorithms within these black boxes has become harder. “Prompt engineering”, as this skill is known, has been likened to guiding a dance partner or poking a beast to see how it will respond. What does it involve?

For starters a good prompt should include a clear instruction: compile a given policy proposal’s potential downsides, for example, or write a friendly marketing email. Ideally the prompt should coax the model into complex reasoning: telling it to “think step by step” often sharply improves results. So does breaking instructions down into a logical progression of separate tasks. To prompt a clear explanation of a scientific concept, for example, you might ask an AI to explain it and then to define important terms used in its explanation. This “chain of thought” technique can also reveal a bit about what is going on inside the model.

AI users need to be able to see that detail. Because big models are trained on what one prompt engineer calls “everything from everywhere”, it helps to include authoritative texts in a prompt, direct a model to give particular sources priority or, at the very least, to tell the model to list its sources. Many models offer settings for “temperature”, which, when raised, increase the randomness of results. That can be good for creative tasks like writing fiction but tends to increase the frequency of factual errors.
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