Yes and every AI-first development workflow worth its salt does exactly this, and it does it much more thoroughly than I’ve ever seen a team of meatbags do it.

My workflow, at a high level, is:

1. I write a high level spec. Not as high level as a single-sentence prompt, but high level enough to capture my top requirements.

2. I prompt the AI to interview me about the spec to clear up any ambiguity or open questions, then when I’m satisfied, the AI writes a longer spec, which I then review.

3. Then I prompt the AI to write an implementation plan based on the spec. I might just skim this, and by this point I might be asking the LLM more questions than it’s asking me.

4. Now I hand it off to the implementer agent.

This isn’t cowboy coding, it’s not even agile. It’s waterfall. The problem with doing waterfall was that it’s too slow, especially with the deserialization/serialization cost of routing all of this documentation through meatbrains. The LLM is doing just as much work, true, but faster.

The thing I found surprising was that, while LLM’s are still pretty awful at writing as an art form, they are better technical writers than I have the time to be, especially when writing for an audience of other LLM’s.