> LLMs _can_ reliably generate (relatively small amounts of) working code from relatively terse descriptions. Code is the detail being filled in.
They can generate boilerplate, sure. Or they can expand out a known/named algorithm implementation, like pulling in a library. But neither of those is generating detail that wasn't there in the original (at most it pulls in the detail from somewhere in the training set).
They do more than that. If you ask for ui with a button that button won't be upside down even if you didn't specify its orientation. Lots of the detail can be inferred from general human preferences, which are present in the LLMs' training data. This extends way beyond CS stuff like details of algorithm implementations.
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