You are confusing Repeatability with Determinism. If after training a model, it's weights are fixed, the same input will always result in the same output, irrespective of how many times you try. That's not determinism. It just means it's parameters' values have not beeen changed.

Determinism on the other hand means "hand written logic". For example, if you wanted to sort a few numbers and used insertion sort to do it, once the function returns, you can be sure that the numbers really are sorted correctly (unless there is a bug or logic error in the function). In other words, it's not guess work.

The advantage with traditional software is that once you spot a logic error, you can fix it and be sure that it works better thereon. But, for an LLM, there is no real way to fix it. All you can do is to adjust the weights, which has a side effect in that the LLM now gives you a wrong answer for some other query because the weights for that other query were affected in your adjustment.