Evaluation data
Evaluation data provides the cases, references, or task settings used to measure model behavior. It can support development decisions or final testing, but those roles should be distinguished. A repeatedly consulted validation set is not equivalent to an independent final test set.
How it works
Define the target population, task, metrics, and success criteria. Select representative cases, include relevant difficult conditions, and document how expected outcomes are determined. For agents, evaluation may inspect resulting environment state rather than compare a short answer.
Prevent overlap with training at the appropriate level: records from the same customer, event, document, or time sequence may be related even if their text differs. Keep preprocessing and model selection from using information that should remain unavailable.
Why it matters for licensing
A dataset can be useful for evaluation without being licensed or suitable for training. The agreement should distinguish those purposes. Documentation should explain benchmark exposure, labels, split construction, and limitations so recipients can interpret scores.
Example
Fictional example: A team assesses a scheduling model on later-period cases held out from training. It checks both schedule validity and unwanted changes, and separates incomplete cases from verified failures.
Limitations and misconceptions
A high score supports conclusions only about the tested setting and criteria. Repeated tuning against a benchmark can weaken its independence. Missing groups, unreliable labels, or leakage can produce unwarranted confidence.
Questions to ask
- Which real use and conditions does this evaluation represent?
- How are training overlap and future information prevented?
- What does each metric establish, and which failures can it miss?
Sources
Explore whether your business data could be a fit.
Start with a description of your systems—not a data upload.