> ## 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.

# Reviews

> Structure expert review and turn feedback into reusable memory

Reviews is the human layer on top of traces. It turns "an expert looked at this and said it was wrong" into a structured, reusable signal, not a comment that dies in Slack.

## Rubric Studio: define what "good" means

Before reviewing, an admin sets up a rubric in **Rubric Studio**: the dimensions to grade on, required fields, positive/negative/ambiguous examples, severity and confidence levels, and the outcomes a reviewer can pick. The same rubric a human uses is later reused to calibrate a [judge](/judges/overview).

## Queues: route work to reviewers

**Queues** control which traces go to which reviewers. Findings can push flagged traces straight into a queue.

## Assignments: do the review

Each reviewer sees their **Assignments**: a queue of traces to grade against the rubric. They submit a correction, a severity, and an outcome.

## Submissions: see what came back

**Submissions** shows completed reviews, so you can see what reviewers found across the board.

## 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.

<Warning>
  Reviews is a UI-only feature today, there's no public API or SDK access yet.
</Warning>

## Next steps

<Card title="Judges & Datasets" icon="scale-balanced" href="/judges/overview">
  Calibrate a judge against this review data
</Card>