Your main target seems to be the view that software quality is a matter of 'aesthetic intuition,' but I see it differently. I think because it's 'engineering,' there must be measurable indicators.

Executable specifications

Unit, integration, and property tests

Official API documentation

All of these provide ways to judge quality. There are so many metrics that the problem is actually choosing which ones to use.

1.Does it produce output A for input B? 2.Can it process 100,000 records within 5ms? 3.Is memory usage within the defined limit? 4.Does the protocol handle error conditions properly?

You don't need to be an expert to test these. People forget that programming is one of the few fields where judgment criteria can be easily translated into machine executable tests.

The biggest problem with epistemological objections is that they often assume a binary: 'experts can judge, non-experts cannot.' But in reality, it's a continuous process.

You run the code, notice something odd, look up the terminology, compare it with official documentation and reference implementations, add tests, and gradually build up judgment in that area. Rewriting LLM generated code compresses that entire learning curve.

In the past, entering an unfamiliar domain meant starting from a blank file and reading documentation. Now, you start with a working hypothesis and modify it. That's far cheaper than starting from scratch.

If you treat LLM generated code as executable teaching material and a falsifiable hypothesis, it's a very powerful resource.

I feel uncomfortable when people reduce programming to aesthetic quality alone. We were all trained to measure things.