* A "best practices" repository: clean code architecture and separation of concerns, well tested, very well-documented
* You need to know the code base very well to efficiently judge if what the AI wrote is sensible
* You need to take the time to write a thorough task description, like you would for a junior dev, with hints for what code files to look at , the goals, implementation hints, different parts of the code to analyse first, etc
* You need to clean up code and correct bad results manually to keep the code maintaineable
This amounts to a very different workflow that is a lot less fun and engaging for most developers. (write tasks, review, correct mistakes)
In domains like CRUD apps / frontend, where the complexity of changes is usually low, and there are great patterns to learn from for the LLM, they can provide a massive productivity boost if used right.
But this results in a style of work that is a lot less engaging for most developers.