1. Solving Data Scarcity
When real-world training examples are rare, proprietary, or privacy-restricted, synthetic data generation provides scalable bootstrapping solutions.
2. LLM-Based Data Augmentation
State-of-the-art LLMs can generate millions of domain-specific edge-case scenarios complete with verified ground-truth labels.
3. Quality Auditing & De-Duplication
Apply automated embedding distance filters to prune duplicate or low-fidelity synthetic generations before training downstream models.
