Design multi-stage review processes to catch annotation errors before they reach your ML training pipeline.
Data Labeling QA Workflow Patterns
- 100% Review - Every annotation reviewed by QA. Highest quality, highest cost.
- Sample Review - Random sampling (e.g., 20% of annotations). Balances cost and quality.
- Risk-Based Review - Review probability based on annotator experience or task difficulty.
- Consensus Labeling - Multiple annotators label the same item. Use majority vote or expert adjudication.
Configuring QA Workflows in TigerLabel
Use the Workflow Editor to create custom multi-stage review processes with conditional routing rules based on:
- Annotator experience level
- Task complexity or confidence scores
- Random sampling percentages
- AI-flagged potential errors