A chainsaw is a physical machine. Physical machines
are technically non-deterministic if you look closely. They have Variance. The discipline to manage variance is called Tolerance.
Many physical machines and components come with a datasheet that will list their tolerances.
Failure to correctly document tolerances does in fact get you sued.
However, while this is truly a great idea, we're not going to be able to make it work for computational systems. Computers, software, and also LLMs are sensitive to initial conditions. Which is why tolerances are not so familiar to computer people. (but not entirely: eg your PSU might list 110-240Vac/300W as input tolerance)
Interestingly, LLMs actually have a somewhat lower sensitivity to initial conditions than traditional interpreters. See what happens if you misspell "What is One Plus nOe?". So they're actually a skosh off the edge and towards the middle, though I'd argue still very much at the computational end, just from the sheer scale of the valid inputs and outputs.
Mind you, if you have a pretrained LLM doing a measurable task on a line, possibly some sort of tolerances could be determined. Not so much when doing arbitrary chat.
Something unintuitive: I bet that often setting the temperature > 0 (aka introduce stochasticity deliberately, variously comparable to dithering or simulated annealing in other disciplines - doing the thing where you escape local minima) will tighten the output tolerance range and improve reliability, especially in iterated processes. This works for a lot of physical and digital processes actually, and LLMs simply stole the same trick.
(edit: I'm trying to compress a huge chunk of dynamics intuition in a few lines here. Hopefully still useful.
TL:DR; Everything real is continuous and noisy if you look close; and you're really trying to build attractors and bound variance, if you can. )