[lsaferite]

I find it's more like that silly experiment where you have to make a sandwich exactly as a kid (or adult) writes the instructions. You _think_ you have a good set of instructions and then you get peanut butter on the outside. So, you revisit the instructions to be clearer about what you want done. That's how I see prompt engineering. In that case, you are simply learning how the model tends to follow instructions and crafting a prompt around that. Not so much random, more purposeful.

[FormerBandmate]

That isn’t the model reasoning. That’s you figuring out exactly what parameters you need to use to make the model give the result you want.

It’s Clever Hans on steroids

[dragonwriter]

> That isn’t the model reasoning. That’s you figuring out exactly what parameters you need to use to make the model give the result you want.

If its to get the model to present a fixed answer, sure.

If its to get a model to do a better job at solving general classes of problems (such as when what you are optimizing is the built-in prompt in a ReAct/Reflexion implementation, not the prompt for a specific problem), that's, at a minimum, different from Clever Hans, even if its not “reasoning” (which is ill-defined).

[dingosity]

As always, it's unclear on which side of the keyboard the intelligence lies.

[Turskarama]

Can you come up with a method that will get you a good response every single time? Because if you can't, it's not really engineering.