> there is no such details.
Qua formal system, yes, but this is a pedantic point as the aim - the what - of a system is more important than the how. This distinction makes the distinction between domain-relevant features and implementation details more conspicuous. If I wish to predict the relative positions of the objects of our solar system, then in relation to that end and that domain concern, it matters not whether the underlying model assumes a geocentric or heliocentric stance in its model (that tacitly is the deeper value of Copernicus's work; he didn't vindicate heliocentrism, he showed that a heliocentric model is just as explanatory and preserves appearances equally well, and I would say that this mathematical and even philosophical stance toward scientific modeling is the real Copernican revolution, not all the later pamphleteer mythology).
Of course, in relation to other ends and contexts, what were implementation details in one case become the domain in the other. If you are, say, aiming for model simplicity, then you might prefer heliocentrism over geocentrism with all its baroque explanatory or predictive devices.
The underlying implementation is, from a design point-of-view, virtually within the composite. The implementation model is not of equal rank and importance as the domain model, even if the former constrains the latter. (It's also why we talk about rabbit-holing; we can get distracted from our domain-specific aim, but distraction presupposes a distinction between domain-specific aim and something that isn't.) When woodworking, we aren't talking about quantum mechanical phenomena in the wood, because while you cannot separate the wood from the quantum mechanical phenomena as a factual matter - distinction is not separation - the quantum is virtual, not actual with respect to the wood, and it is irrelevant within the domain concerning the woodworker.
So, if there is a bug in a library, that is, in some sense, a distraction from our domain. LLMs can help keep us on task, because our abstractions don't care how they're implemented as long as they work and work the way we want. This can actually encourage clearer thinking. Category mistakes occur in part because of a failure to maintain clear domain distinctions.
> That’s not a good comparison. Type checking [...]
It reduces cognitive load vis-a-vis understanding code. When I want to understand a function in a dynamic language, I often have to drill down into composing functions, or look at callers, e.g., in test cases to build up a bunch of constraints in my mind about what the domain and codomain is. (This can become increasingly difficult when the dynamic language has some form of generics, because if you care about the concrete type/class in some case, you need even more information.)
This cognitive load distracts us from the domain. The domain is effectively blurred without types. Usually, modeling something using types first actually liberates us, because it encourages clearer thinking upfront about the what instead of jumping right into how. (I don't pretend that types never increase certain kinds of burdens, at least in the short term, but I am talking about a specific affordance. In any case, LLMs play very nicely with statically-typed languages, and so this actually reduces one of the argued benefits of dynamic languages as ostensibly better at prototyping.)
> As long as you can pattern match to get a solution [...]
Indeed, and that's the point. LLMs work so well precisely, because our abstractions suck. We have lot of boilerplate and repetitive plumbing that is time-consuming and tedious and pulls us away from the domain. Years of programming research and programming practice has not resolved this problem, which suggests that such abstractions are either impractical or unattainable. (The problem is related to the philosophical question whether you can formalize all of reality, which you cannot, and certainly not under one formal system.)
I don't claim that LLMs don't have drawbacks or tradeoffs, or require new methodologies to operate. My stance is a moderate one.