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

# Records

> View, filter, and analyze all evaluation records across your project.

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The **Records** page provides a unified view of all evaluation records across runs in your project. Use it to search, filter, analyze patterns, and bulk re-score records without navigating through individual runs.

<DarkLightImage lightSrc="/images/records-page-light.png" darkSrc="/images/records-page-dark.png" caption="Records page with filtering and history chart." alt="Screenshot of the Records page showing the records table and history chart." />

## What is a Record?

A **Record** is an individual test execution within a run. Each record contains:

* **Inputs**: The data sent to your AI system
* **Outputs**: The response generated by your system
* **Expected (Labels)**: Ground truth or ideal responses for comparison
* **Scores**: Evaluation results from each metric
* **Status**: Whether scoring is pending, completed, or errored

Records are created when you run evaluations via the API, Playground, or from traces.

## Customizing the Table

Click **Edit Table** to customize which columns appear and their order. You can add, remove, and reorder columns including:

* **Base columns**: ID, Created By, Created At
* **Data fields**: Inputs, Outputs, Expected
* **Source**: How the record was created (API, Playground, Kickoff, Trace)
* **Metrics**: Score columns for each metric in your project

<DarkLightImage lightSrc="/images/records-columns-filters-light.png" darkSrc="/images/records-columns-filters-dark.png" caption="Edit Table to customize columns and their order." alt="Screenshot of the Edit Table modal for customizing record columns." />

Your column preferences are saved per project.

## History Chart

The interactive histogram shows record distribution over time. Click any bar to filter records to that time period.

## Bulk Re-scoring

Select multiple records using the checkboxes, then click **Re-score** to re-evaluate them with your metrics. This is useful when:

* You've updated a metric's guidelines
* You want to apply new metrics to existing records
* You need to re-evaluate after fixing a configuration issue

<Tip>
  Re-scoring uses the latest version of your metrics without re-running your AI system.
</Tip>

## Record Details

Click any record to view its full details. The details view differs based on how the record was created:

### Testcase-Based Records

Records created from testsets show:

* **Scores**: Pass/fail status, reasoning, and metric properties for each evaluation
* **Test Record Details**: Input fields, expected outputs, and actual outputs

<DarkLightImage lightSrc="/images/record-details-testcase-light.png" darkSrc="/images/record-details-testcase-dark.png" caption="Testcase-based record showing scores and test details." alt="Screenshot of a testcase-based record with scores and test record details." />

### Trace-Based Records

Records created from production traces show:

* **Trace Overview**: Duration, estimated cost, total tokens, and span count
* **Spans**: Individual LLM calls with timing and cost breakdown
* **Model Usage**: Which models were called and token counts

<DarkLightImage lightSrc="/images/record-details-trace-light.png" darkSrc="/images/record-details-trace-dark.png" caption="Trace-based record showing spans and trace overview." alt="Screenshot of a trace-based record with spans and trace overview." />

## Use Cases

* **Cross-run analysis**: Find patterns across multiple evaluation runs
* **Debugging failures**: Filter by `metric.status:fail` to investigate failing records
* **Quality review**: Review records from specific time periods or sources
* **Metric iteration**: Re-score records after updating metric guidelines