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EvaluationDifference between Datasets and Evaluation Datasets

Difference between Datasets and Evaluation Datasets

Side-by-side comparison of Datasets and Evaluation Datasets and when to use which.

Difference between Datasets and Evaluation Datasets

Both Datasets and Evaluation Datasets live in the same Dataset Library and feed the same experiment pipeline. The difference is whether an experiment needs to generate outputs or just score them.

Comparison

DatasetsEvaluation Datasets
Items have an output columnNoYes (required)
What an experiment doesRuns a prompt or API on each input to generate outputsForwards the captured output straight into the run — no model call
LLM cost per experimentOne call per itemZero
Best forComparing prompts, models, or APIs against the same inputsRe-scoring an existing output set with new evaluators or metrics
Typical source of itemsHand-authored, imported from CSV, or pulled from tracesProduction traces, an offline batch run, or an external system you want to grade
CSV upload requiresinput columninput and output columns
Has Dataset RunsYes — runs group execution results per datasetNo — experiments are the entry point instead
SDK clienttestOps.datasets / client.datasets / sdk.datasets()testOps.evaluationDataset / client.evaluation_dataset / sdk.evaluationDataset()

When to use which

  • Pick a Dataset when you want to compare how different prompts, models, or APIs perform on the same set of inputs. Each experiment will execute the prompt or API per item and produce fresh outputs.
  • Pick an Evaluation Dataset when the outputs already exist and you want to score them — for example, trying new evaluators on yesterday's production responses, replaying a captured set against an updated rubric, or grading outputs from a system you don't want to re-invoke.

Quick rule of thumb: if you can answer "what produces the output for each item?" with a prompt or API in TestAI, use a Dataset. If the outputs already exist and you just want to grade them, use an Evaluation Dataset.