The most useful AI-assisted work starts with a clear technical target. Before asking for implementation help, define the boundary: what should change, what should stay stable, and which users or systems depend on the current behavior.
Understand before accepting
Generated code should be read like a teammate's pull request. Check assumptions, naming, error paths, data ownership, and how the change fits the existing system. If you cannot explain the code after reading it, it is not ready to ship.
The speed gain is real, but only when review quality stays high.
Keep the feedback loop small
Smaller prompts, smaller diffs, and focused verification make AI assistance easier to control. The goal is not to generate the most code at once. The goal is to reach correct, maintainable behavior with fewer dead ends.
Production ownership remains human
When the system fails, the incident does not care how the code was written. Logging, tests, rollback paths, and clear operational behavior still matter. AI is an accelerator for engineering work, not a substitute for engineering responsibility.