---
title: "Label a dataset"
description: "Review trace or session examples against a judge and create a separate dataset with human-expected labels."
sidebar:
  label: "Label a dataset"
seo:
  title: "Label a dataset | Judgment Docs"
  description: "Choose a dataset version and a production judge, record human labels, and create a labeled dataset."
---

Use **Label** on a dataset to record the outputs a judge should return for the
examples in it. Finishing creates a new dataset with the human labels; it
leaves the source dataset unchanged.

## Prerequisites

- A dataset with a `trace` or `session` field. See
  [Build a dataset](/documentation/tests/build-a-dataset).
- A judge with a production version that matches the dataset: a trace judge
  for traces, or a judge with session scoring for sessions.
- Permission to create a dataset.

If you already reviewed judge results on a judge's **Alignment** tab, you can
work from those reviews instead; see [Align a judge](/documentation/judges/align-a-judge).

## Record the expected outputs

1. Open **Datasets & Tests**, select the source dataset, and pick the version
   in the header.
2. Select **Label** in the header.
3. In **Label Dataset**, choose a **Judge**, enter the **New dataset name**,
   and select **Start labeling**.
4. Inspect each example's trace or session. Under **Expected judge result**,
   pick one of the judge's labels (older judges show true/false or a number
   field). Use the evidence and rubric to decide, rather than copying an
   earlier judge result.
5. Move through the examples with **Prev** and **Next** and check the labeled
   count. When ready, select **Create labeled dataset**.

Only labeled examples are included in the new dataset. The footer shows how
many unlabeled examples will be excluded, so finish reviewing every example
first if you need the entire source version represented.

## Verify the new dataset

Open the created dataset and inspect its example count, source fields, and
expected-label column for the selected judge. The column is named after the
judge, for example `expected_order_size` for a judge named "Order size". Check
a few labels against their trace or session evidence before using them as a
test baseline.

The source dataset and selected source version remain available separately.

## Troubleshooting

- If **Label** is absent, the dataset has no `trace` or `session` field, or no
  version is selected.
- If the judge picker says "No compatible judges", no judge has a production
  version that scores the dataset's type (traces or sessions).
- If creation is disabled, record at least one label.
- If the new dataset has fewer examples, check whether some source examples
  were left unlabeled.

## Next step

[Align the judge](/documentation/judges/align-a-judge) against the labeled
evidence before you rely on its output.