but, to be fair, simply calling the sampler random is what gives people the impression like what OP is complaining about. which isn't entirely accurate, it's actually fairly bounded.

this plays back into my original comment, which you have to understand to know that the sampler, for all its "randomness" should only be seeing and picking from a variety of correct answers, i.e. the sample pool should only have all the acceptable answers to "randomly" pick from. so when there are bad or nonsensical answers that are different every time, it's not because the models are too random, it's because they're dumb and need more training. tweaking your architecture isn't going to fully prevent that.