Maintaining a high-quality requirements / specification document for large features prior to implementation, and then referencing it in "plan mode" prompts, feels like consensus best practice at this stage.

However a thing I'm finding quite valuable in my own workflows, but haven't seen much discussion of, is spending meaningful time with AI doing meta-planning of that document. For example, I'll spend many sessions partnered with AI just iterating on the draft document, asking it to think through details, play contrarian, surface alternatives, poke holes, identify points of confusion, etc. It's been so helpful for rapidly exploring a design space, and I frequently find it makes suggestions that are genuinely surprising or change my perspective about what we should build.

I feel like I know we're "done" when I thoroughly understand it, a fresh AI instance seems to really understand it (as evaluated by interrogating it), and neither of us can find anything meaningful to improve. At that point we move to implementation, and the actual code writing falls out pretty seamlessly. Plus, there's a high quality requirements document as a long-lived artifact.

Obviously this is a heavyweight process, but is suited for my domain and work.

ETA one additional practice: if the agent gets confused during implementation or otherwise, I find it's almost always due to a latent confusion about the requirements. Ask the agent why it did a thing, figure out how to clarify in the requirements, and try again from the top rather than putting effort into steering the current session.

You could have a look at: https://github.com/jurriaan/aico

It does 2 things that are very important, 1: reviewing should not be done last, but during the process and 2: plans should result into verifyable specs, preferably in a natural language so you can avoid locking yourself into specific implementation details (the "how") too early.