I think that'd be a worthwhile experiment, but I suspect it might not always be enough (but absolutely worth testing). You can "coerce" the model more strongly

I think that'd be a worthwhile experiment, but I suspect it might not always be enough (but absolutely worth testing). You can "coerce" the model more strongly when you explicitly control everything, though. Adding a loop also allows you to put it in a "straitjacket" in many ways - e.g. if a local model you can select output tokens to force the response to match a given grammar to give it less room to go off the rails (see llama.cpp/llamafile that supports this), you can repeat and adjust temperature until responses match requirements or manipulating the answer so far and have it complete it. Or combine with an evaluation step to force a "does this answer comply with restrictions X, Y, Z: ..." stage, and so on. Of course these are not suitable to get you AGI, but when you have tasks where testing compliance or ranking quality of output are "cheap"/"easy" but coming up with answers is hard, they provide ways to get LLMs to work even in cases where the simple approach of individual, complex prompts are too unreliable. (the tradeoff of course being that if you have to spend too much time tuning these kinds of tricks to a specific problem you need to weigh up if they're worth it vs. solving the problem without an LLM) Of course they are workarounds, and I'm sure there will be architectural advances that will hopefully make these relatively crude methods less important, but in the meantime, they do help.