Feedback

Feedback is how production review improves future evaluations.

Domain reviewers can score outputs, attach corrections, stage useful examples, and promote approved corrections into new datasets.

Who Uses It

• domain experts correcting AI output
• operations teams reviewing issue patterns
• ML and platform teams improving task quality
• risk and compliance teams checking whether incidents feed back into controls

What It Shows

Feedback should show:

• the original inference
• reviewer score
• correction payload
• validation status for the correction
• review status
• task association
• promotion history

Why It Matters

Without feedback promotion, production failures become anecdotes.

With Orlo, reviewed corrections can become future evaluation data, which closes the loop from production evidence back to model selection and deployment quality.