Labeling is where most dataset quality is won or lost. The data can be clean and complete and still produce a weak model if the labels are inconsistent.
Write the rubric first
Before anyone labels a single record, write down exactly what each label means, with edge case examples. A rubric that lives only in someone's head will be applied differently by every reviewer.
Label a small batch, then audit
Run an initial batch of fifty to one hundred records, then review it before scaling up. Catching a flawed rubric after fifty records is cheap. Catching it after ten thousand is not.
Use multiple reviewers on ambiguous cases
For categories with real judgment calls, have more than one person label a sample and check agreement. Low agreement signals the rubric needs to be tightened, not that the reviewers need replacing.
Track labeler-level patterns
Some reviewers default to certain labels under uncertainty. Spot-checking by reviewer, not just by record, catches systematic bias that random sampling can miss.
Version your label schema
When you add or change a label category, document when the change happened. Otherwise you end up with a dataset that silently mixes two different labeling standards.
Good labeling is procedural, not heroic. Teams that document their rubric and audit early consistently outperform teams that just label faster.