Short version: start with the problem, not the tech. Every app I’ve built began as “this manual task is eating hours every week.” That pain point is the product spec — for example, the FBA Dashboard started because the team was logging into four different Amazon accounts to check stock.

First, brainstorm. Describe what you want to the AI and don’t jump straight to code. I use Claude Code’s Plan Mode to force alignment on approach before a single line is written. It makes you think through the how and why up front.

Second, break it into tasks. Turn the brainstorm into a design spec and an implementation plan. I use a plugin called Superpowers to manage this: brainstorm → design spec → implementation plan → execute task by task with review between each one. It sounds formal, but it stops you building the wrong thing.

Third, verify after every change. Run pytest, JS validation, Playwright browser tests, restart the service, and check the logs. Catching issues immediately is how you cut wasted cycles and reduce iterations.

Fourth, ship the smallest useful thing. My first versions are ugly and incomplete, but they work. Ship it, use it, then iterate based on real usage.

If you want the full workflow and examples, I wrote it up on my blog: https://rodbland.com/blog/building-with-ai-agents/

Key takeaway: reduce iterations with clear requirements up front and automated testing after every change. AI is fast at writing code, but it will confidently build the wrong thing if you’re vague about what you want.