Generate, evaluate, iterate: synthetic data to supercharge AI judges
Teach your AI judge—faster—with synthetic test cases
Evaluating AI with "LLM-as-a-judge" works—until you run out of good examples. This study introduces a tool that generates rich, customizable synthetic test cases to refine your evaluation criteria, all within a human-in-the-loop workflow.
- Configure domains, personas, lengths, desired outcomes—even borderline cases.
- AI-assisted inline editing to tweak existing tests.
- Full transparency: see the prompts and explanations used to generate each case.
In a user study (N=24), 83% preferred this tool over manual test creation, citing speed and diversity without extra workload. Crucially, synthetic data was as effective as hand-crafted data for improving evaluation rubrics and aligning with human preferences.
Generate. Evaluate. Iterate. Scale your AI evaluations without sacrificing quality.
Paper: http://arxiv.org/abs/2511.04478v1
Paper: http://arxiv.org/abs/2511.04478v1
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AI LLM SyntheticData Evaluation HumanInTheLoop HCI ML Research AIEvaluation
