Loop engineering: stop prompting, start defining outcomes
A few years ago, I was integrating with an API that shall remain nameless. It told me almost nothing — no error messages, no logs worth reading. Just HTTP status codes and a dashboard with exactly one metric: failed requests over total requests. It started at 1/1 . Then 2/2 . Then 13/13 . Then 32/32 . A perfect record of failure. I was deep in it. Change something, re-send, change something else.…
A failed API integration prompted the author to rethink how to use AI coding tools. Instead of repeatedly prompting for fixes, the author advocated for defining clear outcomes and letting the system iterate towards them. The difference is between prompt engineering—asking once and hoping—and loop engineering—defining success and letting the system figure out how to get there.
A successful loop requires an exit condition the system can verify, clear feedback, boundaries on what it may change, and a budget of attempts. By specifying the desired outcome rather than the code itself, the system can repeat variations with memory, narrowing down the correct solution. The loop's strength lies in its verification mechanism, not in fooling the AI.
Trust is placed in the checking process, which the author wrote and understands fully. While loops can be powerful, they don't apply to tasks like UI design or production migrations that lack clear verification. Loops excel at finding solutions when failed attempts are cheap and the outcome is well-defined.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
