I've noticed this with GPT as well -- the first result I get is usually mediocre and incomplete, often incorrect if I'm working on something a little more obscure (eg, OpenSCAD code). I've taken to asking it to "skip the mediocre nonsense and return the good solution on the first try".
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?
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?