What is an evaluation dataset? Held-out examples for checking task performance
Explain evaluation inputs, references and records and check coverage, leakage and versioning.
Understand the concept
An evaluation dataset pairs samples with references or criteria. Its purpose differs from training or prompt demonstrations and requires independent examples. Size does not establish coverage and incorrect references distort scores. Define behavior, collect normal and difficult inputs and preserve versions.
Samples should represent intended tasks
OpenAI emphasizes real distributions and edge cases and Google combines data with metrics. Easy-only and extreme-only sets misrepresent use. Verify references and allow uncertainty. Open answers may use concrete rubrics rather than exact strings. Define each case’s purpose so failures remain explainable instead of merely accumulating a large table.
Reserve samples not used to tune prompts
For email classification, use some public cases to develop rules and hold others out. Replay held-out examples after changes and retain item-level outputs. Prompting test answers invalidates independence. Keep failures, not only pass rates. No dataset or model run was created here. Resolve annotation disagreements before assigning model errors.
Version datasets and record emerging failures
Add emerging failures while retaining a stable historical subset. Track withdrawals and corrected references and avoid comparing changed criteria directly. Include preparation and judgment effort. Report language, task scope, scoring and gaps. Passing finite examples supports bounded adoption, not permanent error-free behavior.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.