On the other hand, LLMs are highly nondeterministic. They often produce correct output for simple things, but that's because those things are simple enough that we trust the probability of it being incorrect is implausibly low. But there's no guarantee that they won't get them wrong. For more complicated things, LLMs are terrible and need very well specified guardrails. They will bounce around inside those guardrails until they make something correct, but that's more of a happy accident than a mathematical guarantee.
LLMs aren't a level of abstraction, they are an independent entity. They're the equivalent of a junior coder who has no long term memory and thus needs to write everything down and you just have to hope that they don't forget to write something down and hope that some deterministic automated test will catch them if they do forget.
If you could hire an unpaid intern with long term memory loss, would you?
The physics engine in the game Trackmania is deterministic: this means that you can replay the same inputs and get the same output; but it doesn’t mean the output always makes sense: if you drive into a wall in a particular way, you can trigger what’s called an uberbug, where your car gets flung in a somewhat random direction at implausibly high speed. (This sort of thing can lead to fun tool-assisted speedruns that are utterly unviable for humans.)
The abstractions part you mention, there’s the key. Good abstractions make predictable. Turn the steering wheel to the left, head left. There are still odd occasions when I will mispredict what some code in a language like Rust, Python or JavaScript will do, but they’re rare. By contrast, LLMs are very unpredictable, and you will fundamentally never be able to mentally model what it achieves.