I spent over a decade writing software by hand. The last 3 years I've built almost everything with AI, to the point where it builds features based on user behavior and deploys them by itself.

The code was never the problem. AI writes working code on the first try more often than most engineers do. What broke was everything around the code.

None of that is an AI problem. It's software engineering running at ten times the speed, with nobody enforcing the discipline that used to come from typing everything yourself.

After my first 3 large AI-built projects, it clicked. I wasn't missing better prompts, or skills, or memory layers. I was missing a process. So I wrote down everything that consistently worked and everything that kept failing. That turned into 15 stages I now run in order on every build, and give my AI access to.

The lessons that changed the most:

Plan first. A written spec beats a clever prompt every time.

Architect before you build. AI can only scale a codebase designed to scale.

Keep a map of where everything lives, and keep it modular. Clear boundaries, always.

Work in small slices. Each piece expecting the next to arrive later.

Get tests green before you grant more autonomy. Best enforced on a git hook.

Security is a stage, not a cleanup task. Working code isn't safe code.

Build the factory before the car. It keeps things running and handles maintenance too.

Automate last, once the foundation holds. Then you start replacing yourself, in stages.

I wrote all of this down as a handbook, Vibe Coding with Confidence, because I kept watching almost everyone I mentor repeat the same mistakes. They were just missing the reference.

Curious what bit you after the prototype stage, and what you do differently now.