I Deleted 66% of My AI Coding Guide — Here's What Survived

I started with 252 lines of AI coding principles. After four rounds of review, 86 lines survived. But first — a question. Is your team measuring AI coding productivity by any of these? Lines of code generated Number of prompts per session Response speed Commit count Number of AI tools adopted If so, you might be optimizing for the wrong things. Lines-of-code targets reward bloat. Prompt-count targets punish thinking. Speed targets skip verification. When these metrics become goals, quality pays the price. I wrote an 86-line document called The AI Coding Way that explains why — and what to measure instead. It tries to capture what stays true about human-AI coding collaboration, regardless of which tool or model you use. What's in it Three principles , in order of priority: Keep things reversible (prerequisite) — Linters, tests, CI, version control. Safety enables boldness. You can't ask AI to refactor a module if you can't undo it. Make your intent explicit (starting point) — Every cont