> Between the apparently-probabilistic nature of LLMs deciding which instructions ought to be followed

It's not that probabilistic if you want it to be. When sampling from LLMs, you put a temperature parameter, and if it's 0, it will just choose the output which just have the highest probability. It's very large search space, so in practice beam search is used.

- You could read about temperature here: https://nlp.stanford.edu/blog/maximum-likelihood-decoding-wi...)

- You could read about beam search here: https://en.wikipedia.org/wiki/Beam_search