A feedback loop connects production experience with system improvement. Teams observe where artificial intelligence succeeds or fails, capture repeatable patterns and feed those lessons into prompts, tests, retrieval, policies or engineering practice.
Useful feedback needs trustworthy outcome evidence rather than raw activity. Teams should prevent unreviewed model output from automatically reinforcing itself, because that can amplify mistakes or optimize a proxy that does not represent customer value.
Acronyms and aliases
AI feedback loop variantmodel feedback loop variant
Related terms
Frequently asked questions
What information enters an artificial intelligence feedback loop?
It can include reviewed outcomes, failures, customer effects, test evidence, costs and repeatable successful patterns.
What is a risk of an automated feedback loop?
Unverified outputs can reinforce errors or optimize the wrong metric if they are fed back without review and outcome checks.
