GANs are a general tool -- they just happen to get a lot of attention for generating images of stuff. Here's an example for generating sequences [1]. The example is language oriented, but ultimately GANs are interesting because you can use them to build a generator for an arbitrary data distribution. This can have many applications in engineering (to take a random example -- generating plausible looking chemical structures under a certain set of constraints). As with any ML application, you need to quantify your tolerance for "inaccuracy" (in a generative setting, how well the generated distribution matches the true data distribution). This is simply an engineering trade-off and will vary based on the application.
The approach was applied without any real knowledge of art, even though it has been applied to the domains you mentioned I don't see why not.
[edit]: it is a lot harder to build a NN when there are very constraint rules. But it is also a lot easier to verify and penalize it and generate synthetic data.
Does this only apply to artistic content, or also to engineering content ? Say PCB layouts, architectural plans, mechanical designs, etc ?