> ## Documentation Index > Fetch the complete documentation index at: https://docs.dataframer.ai/llms.txt > Use this file to discover all available pages before exploring further. # The operational loop > Assign AI traces to reviewers, grade them against the rubric, and reuse every correction. With a rubric in place, reviews become repeatable: assign right traces to the right people, grade them, see where reviewers agreed and disagreed, and feed the results back into the system. ## Assign traces to reviewers **Queues** control which traces go to which reviewers. Findings can push flagged traces straight into a queue. From there the work spreads itself. A queue carries the rubric to grade against and the reviewers on the team, and it hands traces out round-robin, so no one assigns reviews by hand and the load stays even across the team. ## Do the review Each reviewer sees their **My Reviews** tab: a queue of traces to grade against the rubric. They can submit corrections on different sections, rationales, and other grades. They work in a grading workbench that sits right next to the trace. They score each dimension, set their confidence and severity, add a short rationale, and can edit the model's answer side by side to capture what the right response should have been. A [built-in copilot](/reviews/copilot) drafts the correction and the rationale for each grade, so reviewers check and approve instead of writing from scratch. ## See where reviewers agreed and disagreed **All Reviews** shows completed reviews across all reviewers, so you can see what they found across the board. Because more than one expert can grade the same trace, you can also see where they agree and where they don't, which is the same signal that tells you how solid your ground truth is. ## Feedback gets reused automatically A reviewer's correction isn't a one-off. It's distilled into memory that speeds up future reviews of similar cases, and feeds directly into [judge calibration](/judges/overview) and [regression datasets](/concepts), so the same mistake, once caught, doesn't need catching twice. ## Next steps Calibrate a judge against this review data