Where Human Review Belongs in an AI Workflow
Introduction Human review is most effective when it is designed into the process rather than added after an AI system makes a mistake. The workflow should explain which cases need review, what information the reviewer sees, and how the system continues after a decision. In multilingual feedback rou
Posted on Oct 25 • Originally published at kriyvoai.online
Human review is most effective when it is designed into the process rather than added after an AI system makes a mistake. The workflow should explain which cases need review, what information the reviewer sees, and how the system continues after a decision.
In multilingual feedback routing, an uncertain classification may need attention before a ticket is sent to a team. The same principle applies when model output influences another customer-facing or operational action.
Possible triggers include missing required information, an unsupported language, an ambiguous category, disagreement between validation rules, or an action with significant consequences. Do not depend on a model's confidence value alone unless it has been calibrated and evaluated for the specific task.
Show the original input or a protected reference, the proposed result, the relevant rule, and the reason the workflow paused. Let the reviewer correct or reject the suggestion. Avoid presenting a model-generated answer as an established fact.
Record the reviewer's decision and resume from a known workflow state. If the case cannot be resolved, make the next owner explicit. Use stable identifiers so a retry or reviewer action does not create duplicate tickets or notifications.
Measure cases sent for review, time awaiting a decision, common correction reasons, and unresolved cases. Do not treat a low review rate as success unless automated outcomes are also checked. The BMRC feedback-routing case study describes its operational scope.
Originally published at Kriyvo AI: https://kriyvoai.online/blog/human-review-ai-workflow-design