This is similar to how I use LLMs (architect/plan -> implement -> debug/review), but after getting bit a few times, I have a few extra things in my process:

The main difference between my workflow and the authors, is that I have the LLM "write" the design/plan/open questions/debug/etc. into markdown files, for almost every step.

This is mostly helpful because it "anchors" decisions into timestamped files, rather than just loose back-and-forth specs in the context window.

Before the current round of models, I would religiously clear context and rely on these files for truth, but even with the newest models/agentic harnesses, I find it helps avoid regressions as the software evolves over time.

A minor difference between myself and the author, is that I don't rely on specific sub-agents (beyond what the agentic harness has built-in for e.g. file exploration).

I say it's minor, because in practice the actual calls to the LLMs undoubtedly look quite similar (clean context window, different task/model, etc.).

One tip, if you have access, is to do the initial design/architecture with GPT-5.x Pro, and then take the output "spec" from that chat/iteration to kick-off a codex/claude code session. This can also be helpful for hard to reason about bugs, but I've only done that a handful of times at this point (i.e. funky dynamic SVG-based animation snafu).