You can make a better tool by training the service (some of which involves training the model, some of which involves iterating on the prompt(s) behind the scene) to get a lot of the iteration out of the way. Instead of users having to fill in a detailed prompt we now have "reasoning" models which, as their first step, dump out a bunch of probably-relevant background info to try to push the next tokens in the right direction. A logical next step if enough people run into the OP's issue here is to have it run that "criticize this and adjust" loop internally.

But it all makes it very hard to tell how much of the underlying "intelligence" is improving vs how much of the human scaffolding around it is improving.