Building a Prompt Engineering Feedback Loop: The System That Made My AI Prompts 3x More Effective

Most developers treat prompt engineering like a one-time skill. You read a guide, learn a few tricks, then wing it from there. That is how I started too. It did not work. I run an AI automation agency. I use the Claude API and Claude Code daily for production systems, everything from generating content at scale to building full-stack features. When your prompts power revenue-generating infrastructure, "good enough" prompts cost real money in wasted tokens, bad outputs, and manual rework. So I built a feedback loop. After three months of running it, I have 9 reusable prompt templates, 6 saved examples I reference constantly, and a documented list of anti-patterns that would have kept burning me. Here is the system. The Rating Schema After every meaningful AI session, I spend 60 seconds recording a rating. The key is making this fast enough that you actually do it. Rating scale (1-5): Score Meaning Trigger 1 Unusable Output required complete rewrite or was factually wrong 2 Poor Correct