The separation of planning and execution resonates strongly. I've been using a similar pattern when building with AI APIs — write the spec/plan in natural language first, then let the model execute against it.

One addition that's worked well for me: keeping a persistent context file that the model reads at the start of each session. Instead of re-explaining the project every time, you maintain a living document of decisions, constraints, and current state. Turns each session into a continuation rather than a cold start.

The biggest productivity gain isn't in the code generation itself — it's in reducing the re-orientation overhead between sessions.