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.
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.
Searching and Filtering
Search through trace metadata to find specific records. Click the search field and enter any text that appears in your trace metadata. This search looks through all metadata fields and returns records from matching traces.
Metadata search uses ClickHouse for high-performance searches across large datasets. Results may take a few seconds to load for very large projects.
Filtering Options
Use the filter dropdown to narrow results by:
- Run: Filter by specific evaluation run
- Source: How the record was created (API, Playground, Kickoff, Trace)
- Status: Scoring status (completed, pending, errored)
- Date range: Records created within a specific time period
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
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
Re-scoring uses the latest version of your metrics without re-running your AI system.
Record Details
Click anywhere on a table row to view the full record details. You can also click the specific record ID link if you prefer. The entire row is clickable to improve discoverability.
Interactive elements like checkboxes, score cards, and popover buttons won’t trigger navigation - only clicking on empty areas of the row will open the record details.
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
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
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
