Generate synthetic training data when you do not have enough real examples. Use when you are starting from scratch with no data, need a proof of concept fast, have too few examples for optimization, cannot use real customer data for privacy or compliance, need to fill gaps in edge cases, have unbalanced categories, added new categories, or changed your schema. Also used for create training data wi
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Generate Synthetic Training Data
Guide the user through generating high-quality synthetic training data with DSPy. This solves the "I do not have data" problem that blocks every other AI workflow.
When NOT to generate synthetic data
You have enough real data — 200+ labeled examples is usually enough for optimization. Real data is always better than synthetic.
Exact-match tasks — if your task has a known correct answer (math, lookup, structured extraction from templates), writ
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