At the risk of stretching the analogy, the LLM's internal representation is that theory: gradient-descent has tried to "explain" its input corpus (+ RL fine-tuning), which will likely contain relevant source code, documentation, papers, etc. to our problem.
I'd also say that a piece of software is a theory too (quite literally, if we follow Curry-Howard). A piece of software generated by an LLM is a more-specific, more-explicit subset of its internal NN model.
Tests, and other real CLI interactions, allow the model to find out that it's wrong (~empiricism); compared to going round and round in chain-of-thought (~philosophy).
Of course, test failures don't tell us how to make it actually pass; the same way that unexpected experimental/observational results don't tell us what an appropriate explanation/theory should be (see: Dark matter, dark energy, etc.!)
Vibing gives you something like the geocentric model of the solar system. It kind of works but but it's much more complicated and hard to work with.
I guess the current wave is going to give us Sofware Development Epicycles (SDEC?)
* All analogies are "wrong", some analogies are useful
Obviously the author has to do much work in selecting the correct bits from this baggage to get a structure that makes useful predictions, that is to say predictions that reproduces observable facts. But ultimately the theory comes from the author, not from the facts, it would be hard to imagine how one can come up with a theory that doesn't fit all the facts known to an author if the theory truly "emanated" from the facts in any sense strict enough to matter.