Explore practical methods, tradeoffs, and evaluation approaches for using synthetic data to augment scarce datasets safely.
Practical guide to when and how to use synthetic data for scarce or sensitive datasets: generation approaches, labeling propagation, quality checks, evaluation strategies against held-out real data, governance, and a compact experimental workflow teams can try.
A practical, team-oriented primer that explains when synthetic data helps, common generation and augmentation methods, a step-by-step workflow for safe use, concrete validation checks, evaluation metrics, quick experiments to try, and common pitfalls with mitigations. Designed to help practitioners use synthetic data to increase coverage, reduce labeling cost, and preserve privacy while avoiding bias and unrealistic artifacts.
Short, actionable research briefs that describe promising AI opportunities, propose bounded experiments, identify required data and tooling, surface likely risks, and suggest measurements to decide whether to scale.