Lesson 1 · 9 min
Why synthetic data — and when it backfires
Synthetic data generated by LLMs is now a first-class tool for fine-tuning, evaluation, and augmentation. But it has failure modes that real data doesn't. Learn when to use it and when to stay away.
The data bottleneck
Every fine-tuning project eventually hits the same wall: the model quality is limited by data quantity or quality, not by the model architecture. Real labeled data is expensive, slow to collect, and often unevenly distributed — you have thousands of common cases and ten examples of the rare failure mode that causes the most support tickets.
Synthetic data generated by a capable LLM (Claude, GPT-4o) can break this wall:
- Scale — generate 10,000 examples in hours instead of months
- Coverage — explicitly generate examples for underrepresented cases
- Privacy — generate examples that look like real data without containing real PII
- Iteration — generate a new variant of the training set every time your task definition evolves