When LLMs started being somewhat useful for coding a few years ago, and I found they were in fact great at boilerplate, in fact pretty much only good at boilerplate ca 2023 or so, it got me thinking about all the accommodations we make in design and systems architecture that are sort of tacitly understanding who we're working with and their strengths and weaknesses.
The modern models have their own very different strengths and weaknesses compared to humans, and deploying them is a really interesting exercise of different architectural and engineering skills. I've enjoyed it, and hope I continue to.
I'd much rather django-admin startproject, npm init, or meteor create and get deterministic output than prompt an LLM and get who knows what.
In a mature web ecosystem, boilerplate is minimal. I worry now that we've given this task to LLMs, less development effort will go into startproject-esqe CLIs and good opinionated defaults.
You're better off plonking down an existing framework and getting all the structural boilerplate benefits the LLM can leverage.
LLMs are far better at frameworks they have a lot of training data for, if have been around for a while. They write more idiomatic, ecosystem friendly code. Does that still matter?