Labelled data is the real bottleneck in most student fine-tuning projects, and generating synthetic examples with a larger model is the obvious shortcut. It works — with conditions.
It works when
- You have real examples to seed and validate against, even if there are only a few dozen.
- The task has a checkable structure (format, classification labels) so bad generations are easy to filter.
- You mix synthetic with real data rather than training purely on generated examples.
It fails when
The generating model's own errors — a subtly wrong fact, a biased phrasing, a format quirk — get copied into every synthetic example, and your fine-tune learns to reproduce that error confidently and consistently. This is worse than random noise because it is systematic.
Always hold out a small real-data validation set the synthetic data never touches, and check final model quality against it, not against the synthetic distribution. See dataset validation before training.
— Pranjul Rathour, GenAI Engineer from Kanpur, India. Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at any campus: pranjulrathour41@gmail.com.