The current crop of LLM are better aware of all the state of the art in NN architecture than yours-truly. They know things such as the number of layers used for

The current crop of LLM are better aware of all the state of the art in NN architecture than yours-truly. They know things such as the number of layers used for X, Y and Z, the class of cost function which is popular with a specific architecture, dropout-vs-no-dropout, etc. That kind of knowledge still needs a human engineer to be leveraged, but even now it seems it's more because nobody has built a harness for the LLM to be able to put together neural network architectures. And, there are already automatic constraint checkers for NN architectures, so even if an LLM hallucinates something wrong, constraint checks could be used to automatically call its attention to some design flaws.