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), write a script to generate test cases programmatically instead of using an LM.
• The LM does not understand your domain — synthetic data inherits the generator LM's biases. For highly specialized domains (medical, legal, niche industry), a few real expert-labeled examples outweigh hundreds of synthetic ones.

Step 1: Understand the data gap

Ask the user:

1. What does your AI do? (classification, extraction, Q&A, generation?)
2. How many real examples do you have? (zero, a handful, or hundreds with gaps?)
3. What is the gap? (no data at all, missing categories, edge cases, privacy constraints?)
4. What format are the inputs/outputs? (text in/category out, text in/JSON out, etc.)

Step 2: Define what an example looks like