[noogle]

"prompt engineering" is a self-destructing field. If you use any rigorous approach to optimizing the prompt, you end up with essentially supervised machine -learning: models can (and do) learn the optimal prompt once there is a yardstick for the goodness of the model's response. That's a classical for a data-scientist, but the skill set has little to do with prompts.

If you are not rigorous, then what you are doing is essentially "black art". It may work for some tasks ad-hoc, but with the rapid pace of model improvement your skill will likely become irrelevant/not needed quickly.

[vidarh]

Focusing on specific incantations, yes. Focusing on how to decomposing a problem, probably not, but then you get very close to designing systems / data design and analysis methodologies more than "prompt engineering", so I guess I mostly agree with you in as much as the relevant field is not really about AI as it is about picking up more structured design and analysis practices (again).

[unaindz]

I don't have much experience with GPT but with image generation.

You need some amount of experimentation to get the best results but in my experience what works for one model does nothing or worsens the output in others. Adding loras and different types of images into the equation makes this so variable that I would never consider it useful besides keeping a few key words I used to get x good result on y model and experimenting with those when I start a new project.

Calling it "prompt engineering" seems odd.