@Saboo_Shubham_: Loop engineering cycle for AI Product Managers. For the last two years PMs have been trying to write the perfect prompt…

Loop engineering cycle for AI Product Managers. For the last two years PMs have been trying to write the perfect prompts. The better move is to stop prompting one-off and start building loops. A loop is a system that improves every time it runs. At the center is a reusable artifact. A PRD-review skill, an eval rubric, a launch checklist. The thing you reuse across dozens of runs, not a prompt you throw away after one. Here is the cycle. 1. Change the artifact Add a review criterion. Remove a stale instruction. One small edit that shapes how the agent behaves. 2. Run the agent Point it at real work. Review a PRD. Summarize ten calls. Whatever the artifact is built to do. 3. Evaluate the output This is the quality gate. Not "did it sound smart." Is it better than the last version? Did it catch the real gaps? Did the evidence hold up? 4. Keep or revert If quality went up, keep the change. If it dropped, revert. The gate decides, not your gut. 5. Commit the learning The change goes into version history. The commit, the diff, the decision log. The repo becomes product memory. 6. Better version next The next run does not start from zero. It starts ahead of the last one. That is the whole point. Prompting helps you do the task once. Loop engineering improves the thing that does the task every time. The agent runs the loop. You own whether it keeps getting better. Read the full article to learn more.