> ## Documentation Index
> Fetch the complete documentation index at: https://www.adaline.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Annotate logs

> Curate production evidence after adding spans to datasets

Annotations turn copied production evidence into useful evaluation data. After a span becomes a dataset row, add the labels, expected behavior, notes, or review status your team needs to trust it.

## Where annotation happens

Adaline keeps raw evidence in [Traces](/docs/monitor/analyze-log-traces), but review work belongs in [Datasets](/docs/datasets/overview). Use logs to find the case, then use dataset columns to capture what the team learned.

Typical annotation fields include:

| Field               | Purpose                                                                              |
| ------------------- | ------------------------------------------------------------------------------------ |
| **Expected output** | The response or behavior future prompt versions should produce.                      |
| **Issue label**     | The failure mode, such as wrong tool, missing context, unsafe answer, or bad format. |
| **Review note**     | Human explanation of what happened and why the row matters.                          |
| **Priority**        | Whether the case should block release or remain advisory.                            |
| **Status**          | Pending, reviewed, fixed, or accepted risk, based on your team's workflow.           |

Column names are flexible. Keep them consistent across datasets so evaluators and reviewers do not have to relearn each table.

## Build a review queue

Use dataset filters to create a small queue, not a giant backlog:

1. Add only representative spans from Monitor, Traces, Behaviors, or Improve review.
2. Filter dataset rows where review fields are empty.
3. Annotate the rows that will affect release decisions.
4. Attach evaluators that use the expected output, label, or rubric.
5. Move reviewed rows into regression coverage when they should gate future prompt versions.

## Keep annotations practical

Write enough for another teammate to understand the case, but do not turn every row into a long incident report. The best annotations explain what the model should do differently and why the row belongs in the dataset.

<CardGroup cols={2}>
  <Card title="Build datasets from logs" icon="database" href="/docs/monitor/build-logs-from-dataset">
    Add useful production spans to datasets.
  </Card>

  <Card title="Set up a dataset" icon="table" href="/docs/evaluate/setup-dataset">
    Structure dataset columns for review and evaluation.
  </Card>

  <Card title="Evaluators overview" icon="flask-conical" href="/docs/evaluators/overview">
    Turn annotated expectations into checks.
  </Card>

  <Card title="Build regression coverage" icon="shield-check" href="/docs/monitor/build-logs-from-dataset">
    Use production cases as release safety.
  </Card>
</CardGroup>