Feedback Loop

Why it matters

Teams ship AI features without closing the loop - they launch, hope for the best, and never measure whether outputs improved decisions. Individuals study without testing themselves. The Feedback Loop explains why labelled data, evaluation metrics, retrospectives, and iteration velocity matter more than perfect first attempts.

• A loop needs a clear goal, a measurable signal, and a mechanism to change behaviour based on that signal.
• Delay in the loop increases error - fast feedback beats perfect feedback that arrives too late.
• Positive feedback amplifies; negative feedback corrects - both must be intentional, not accidental.
• The loop can be human-in-the-loop (review, label, edit) or fully automated (metrics, retraining triggers).
• Without closing the loop, you are running open-loop - acting on assumptions that never get tested.

1. 1

   Define one metric that tells you whether the system is improving (accuracy, time saved, user correction rate).

2. 2

   Instrument the loop: log inputs, outputs, and human overrides so you can review patterns weekly.

3. 3

   Shorten cycle time - ship a small version, measure, adjust, rather than waiting for a perfect launch.

4. 4

   Separate exploration (trying new approaches) from exploitation (optimising what already works) across loop cycles.

5. 5

   For personal learning, add a test step after every study session: explain aloud, solve a problem, or teach someone else.

Real-world examples

• Measuring vanity metrics that do not connect to the goal (clicks vs actual task completion).
• Closing the loop only on successes - failures often carry the richest adjustment signals.
• Adding feedback without authority to act on it (teams report problems but cannot fix root causes).
• Confusing a one-time evaluation with a continuous loop - models and user needs drift over time.