Definition: Human-in-the-loop (HITL) is a design approach in which people review, approve, correct or label an AI system's work at defined points, so automation handles volume while humans keep control of important decisions.
The system routes certain cases to a person: actions above a risk or value threshold, outputs with low confidence, or a random sample for quality checks. Reviewers approve, edit or reject, and their decisions are logged. Those corrections often become new evaluation or training examples.
HITL reduces the impact of errors and hallucinations, supports accountability in regulated work, and builds trust with the teams who use the system. It is a common control in AI governance programs.
Good review steps are designed, not bolted on: clear queues, the right context shown to the reviewer, measured turnaround and a plan to reduce review as accuracy is proven. Teams track how often reviewers override the AI to decide when autonomy can grow.
Ryz AI pod teams build review workflows into production AI systems, such as fraud scoring that is validated by a client's own fraud team, working on your team.
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