There's a sweet spot in terms of generalization. Yes, painstakingly writing out an object definition in English just so that the LLM can write it out in Java is a poor use of time. You want to give it more general tasks.
But not too general, because then it can get lost in the sauce and do something profoundly wrong.
IMO it's worth the effort to know these tools, because once you have a more intuitive sense for the right level of abstraction it really does help.
So not "make this very basic data structure for me based on my specs", and more like "rewrite this sequential logic into parallel batches", which might take some actual effort but also doesn't require the model to make too many decisions by itself.
It's also pretty good at tests, which tends to be very boilerplate-y, and by default that means you skip some cases, do a lot of brain-melting typing, or copy-and-paste liberally (and suffer the consequences when you missed that one search and replace). The model doesn't tire, and it's a simple enough task that the reliability is high. "Generate test cases for this object, making sure to cover edges cases A, B, and C" is a pretty good ROI in terms of your-time-spent vs. results.