Synthetic Data Generation

Synthetic data uses a strong model to manufacture training or evaluation examples when human labels are scarce or expensive: instruction/response pairs, hard edge cases, distillation targets. It works when you bolt on real quality controls (dedup, filtering, diversity, verification) and fails quietly when you skip them, because you can poison your own training set and contaminate your own benchmarks.