[lee_ars]

> It's the same programming with LLMs. Through experience, you build up intuition and rules of thumb that allow you to get good results, even if you don't get exactly the same result every time.

Friend, you have literally described a nondeterministic system. LLM output is nondeterministic. Identical input conditions result in variable output conditions. Even if those variable output conditions cluster around similar ideas or methods, they are not identical.

[ux266478]

The problem is that this is completely false. LLMs are actually deterministic. There are a lot more input parameters than just the prompt. If you're using a piece of shit corpo cloud model, you're locked out of managing your inputs because of UX or whatever.

[troupo]

Ah, we've hit the rock bottom of arguments: there's some unspecified ideal LLM model that is 100% deterministic that will definitely 100% do the same thing every time.

[lee_ars]

> The problem is that this is completely false. LLMs are actually deterministic. There are a lot more input parameters than just the prompt. If you're using a piece of shit corpo cloud model, you're locked out of managing your inputs because of UX or whatever.

When you decide to make up your own definition of determinism, you can win any argument. Good job.

[jonas21]

Yes, that's my point. Neither driving nor coding with an LLM is perfectly deterministic. You have to learn to deal with different things happening if you want do do either successfully.

[troupo]

> Neither driving nor coding with an LLM is perfectly deterministic.

Funny.

When driving, I can safely assume that when I turn the steering wheel in the direction in turns. That the road that was there yesterday is there today (barring certain emergencies, that's why they are emergencies). That the red light in a traffic light means stop, and the green means go.

And not the equivalent "oh, you're completely right, I forgot to include the wheels, wired the steering wheel incorrectly, and completely messed up the colors"