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* diagnosis
* decomposition
* reframing
* constraint design.
Diagnosis is discovering the problem that AI can solve. This is the human part of knowing that a problem exists. Learning to ask the right questions, look at the different ways that the problem can be seen.
Decomposition is about splitting the big problems into bite-sized ones. Take the problem apart, examine it, and let AI help you determine your findings since it handles data so well. Instead of tackling the biggest problem, take it apart and work on the smaller parts to achieve small successes.
Reframing is about shifting your perspective and seeking new interpretations. Extrapolating and recombining the parts of the problem in order to identify the meta components. Perhaps a new way of looking at the problem may find a solution hidden in plain sight.
Constraint design is about setting boundaries for the solution. Knowing what to accomplish, and when to know it is done. Setting the length, style, and description of the audience can help AI understand its mission. But we have to know that first in order to instruct.
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As someone who asks GPT and junior developers for lots of things, there are a lot of similarities. I don't imagine that is going away, at least until we wire LLMs up to a huge amount of rapidly changing, cross-silo context so it could understand "Fix the monitoring that slowed our recognition of yesterday's bug". So being thoughtful isn't going away. The author agrees with that (see above), but doesn't make clear where he draws the boundary between "being thoughful" and "prompt engineering"