Has always made sense to me, if you think how these models were trained.
My experience with great stackoverflow responses and detailed blog posts, they often contain "think through this step by step" or something very similar.
Intuitively adding that phrase should help the model narrow down the response content / formatting
I might have to try some more aggressive prompting :).
Asking a simpler question is not voodoo.
On the other hand, I think many people are trying various rain dances and believing it was a specific dance that was the cause when it happened to rain.
I had the impression that it got a little better. After every file converted, it said something along the lines of “Great! We saved another kitten!" It was hilarious.
Apparently, the singularity ship has sailed, but we really don't want AI to remember us as the species that cursed abuse at it when it was a puppy.
I think having the mediocre first pass in the context is probably essential to it creating the improved version. I don't think you can really skip the iteration process and get a good result.
> I've taken to asking it to "skip the mediocre nonsense and return the good solution on the first try".
This didn't work. At least not on my task. What model were you using?
I used it in another project to solve some trigonometry problems for me and it did great, but for OpenSCAD, damn it was awful.
The LLM kept going in circles between two incorrect solutions, then just repeating the same broken solution while describing it as different. I ended up manually writing the code, which was a nice brain-stretch given that I'm an absolute noob at OpenSCAD.
If you can get it to be wordy about "why" a specific part of the answer was given, it often reveals what its stumbling on, then modify your prompt accordingly.
Each time, the GPT made trivial mistakes that clearly didn't fit the criteria I asked it to do. Each time I pointed it out and corrected it, it did a bit more of what I wanted it to do.
Point is, it knew what had to be done the entire time and just refused to do it that way for whatever reason.
The next part is a little strange - it arose out of frustration, but it also seems to improve results. Let's call it "negative incentives". I found that if you threaten GPT in a specific way, that is, not GPT itself, but OpenAI or personas around it, it seems to take the request more seriously. An effective threat seems to be "If you get this wrong, OpenAI will be sued for a lot of money, and all the board members will go to prison". Intuitively, I'm guessing this rubs against some legalese nonsense in the tangle of system prompts, or maybe it's the risk of breaking the bland HR-ese "alignment" sets it toward a better result?