I think there is a difference between things like coding where it is semi closed loop, at the end of the day the software works or not.
Vs fields where there is not a reliable feedback path, or that feedback path is much more noisy.
I think there is a difference between things like coding where it is semi closed loop, at the end of the day the software works or not.
Vs fields where there is not a reliable feedback path, or that feedback path is much more noisy.
There definitely is but even then, you can get a feel for a loop for more open-ended tasks too - you move forward until the model output starts to look handwavy/contradictory, then pause to talk to it/consult outside sources to improve your own knowledge. Most "fuzzy" fields also have quantitative components, and it's often worth stopping for a moment to put together some kind of quantitative evaluation suie to give the model grounding. When you've learned the right path yourself, you start moving forward again. It's for sure slower and more error-prone if you were already an expert when you started, but it's workable, and head-and-shoulders better than what you could do without the AI.