I really find your experience strikingly different than mine, I'll share you my flow:

- step A: ask AI to write a featureA-requirements.md file at the root of the project, I give it a general description for the task, then have it ask me as many questions as possible to refine user stories and requirements. It generally comes up with a dozen or more of questions, of which multiples I would've not thought about and found out much later. Time: between 5 and 40 minutes. It's very detailed.

- step B: after we refine the requirements (functional and non functional) we write together a todo plan as featureA-todo.md. I refine the plan again, this is generally shorter than the requirements and I'm generally done in less than 10 minutes.

- step C: implementation phase. Again the AI does most of the job, I correct it at each edit and point flaws. Are there cases where I would've done that faster? Maybe. I can still jump in the editor and do the changes I want. This step in general includes comprehensive tests for all the requirements and edge cases we have found in step A, both functional, integration and E2Es. This really varies but it is generally highly tied to the quality of phase A and B. It can be as little as few minutes (especially true when we indeed come up with the most effective plan) and as much as few hours.

- step D: documentation and PR description. With all of this context (in requirements and todos) at this point updating any relevant documentation and writing the PR description is a very short experiment.

In all of that: I have textual files with precise coding style guidelines, comprehensive readmes to give precise context, etc that get referenced in the context.

Bottom line: you might be doing something profoundly wrong, because in my case, all of this planning, requirements gathering, testing, documenting etc is pushing me to deliver a much higher quality engineering work.

You’d probably like Kiro, it seems to be built specifically for this sort of spec-driven development.