Unlike when I am prototyping a solution using tools, concepts, and frameworks that I already understand, when I am trying to work in an area where I cannot be a reliable judge, I tend to iterate much more carefully. There’s really two frames that I am under when working with Models and they are mostly mutually exclusive. I try to increase either speed of:
- Output artifact creation by leveraging my expertise and ability to Judge faster than I can Do.
- Input knowledge acquisition by leveraging the Model’s ability to Explain faster than I can Do.
So in the second frame, the primary goal is that I bring myself up to speed until I feel comfortable defending the directions we have taken. Yes sometimes its just “well that’s the best I can understand this for now”, but when the session is over I am one step closer to reaching the first frame.
The downside is that there are fewer times where I am learning purely through discovery. Often, I am using some other knowledge I already have, to inform whether I should push back/dive in or accept the Model’s Justifications.
Honestly, I think for most things I have been learning lately thats ok. It’s not too different from learning by reading a textbook isntead of just pure experimentation.
As I come to experiment more downstream of that acquisition, I can deviate from the standard approach. For the time being, I’m content probing the Model for what it considers “best practices”.