Generate Synthetic Data with LLMs

When data is the bottleneck

LLMs can generate labeled examples to seed a classifier, build an eval set, or cover rare edge cases.

Generate 10 diverse customer support questions about "billing",
each with a short expected category label, as JSON.

Use with care

Synthetic data can be repetitive or subtly biased toward the model's style. Mix in real data, deduplicate, add diversity constraints, and validate a sample by hand. It's a powerful jumpstart — not a full replacement for real data.