What Belongs In A Dataset
Keep rows specific. A dataset row should make it clear what input is being tested and what good output looks like.
How Datasets Work
Dataset columns usually map to prompt variables. If a prompt expects{{request_genre}}, the dataset should have a request_genre column. Extra columns can hold expected output, labels, notes, IDs, or evaluator context.
Columns can be:
Datasets can also contain text, images, and PDFs. Use multimodal cells when your prompt consumes files or visual context.
Common Workflows
1
Create or import rows
Add rows manually, upload a CSV, or copy a useful production span from Monitor.
2
Match columns to prompt variables
Make sure required prompt variables have matching dataset columns.
3
Add expected output or labels
Store what the evaluator should check, what the reviewer should notice, or why the case matters.
4
Run evaluations
Use the dataset with one or more evaluators to score prompt output.
5
Keep useful failures
Promote important production failures or Improve evidence into long-lived regression coverage.
Where Datasets Fit
Datasets connect the rest of the Platform:- Prompts use dataset rows as repeatable inputs.
- Evaluators score prompt responses against dataset cases.
- Monitor turns real production spans into dataset rows.
- Behaviors reveal repeated patterns worth preserving as coverage.
- Improve uses linked and generated datasets to compare prompt candidates before review.