Training-ready data

Preparation can include schema validation, normalization, deduplication, annotation, sensitive-content review, and separation of training from evaluation data. The team should test a sample with the intended pipeline and document assumptions, exclusions, and known limitations.

Readiness criteria differ between tasks. An image collection, a preference dataset, and a sequence of software actions require different representations and checks. A delivery that passes a file-format test can still fail the substantive requirements.

A licensing discussion should specify who performs preparation and how acceptance will be evaluated. That avoids treating a raw export and a validated task-specific dataset as the same deliverable. Preparation effort also affects cost and schedule without guaranteeing a training benefit.

Fictional example: A recipient needs labeled request-response pairs. A business’s ticket archive becomes a candidate only after conversation boundaries, label rules, permissions, and evaluation separation are assessed. Renaming the export “training data” would not accomplish those steps.

There is no universal threshold for training readiness. Data can pass technical checks yet be unrepresentative, legally restricted, or unsuitable for a new task. Actual model evaluation remains necessary after training.