ai-generating-data
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:
- What does your AI do? (classification, extraction, Q&A, generation?)
- How many real examples do you have? (zero, a handful, or hundreds with gaps?)
- What is the gap? (no data at all, missing categories, edge cases, privacy constraints?)
- What format are the inputs/outputs? (text in/category out, text in/JSON out, etc.)
Step 2: Define what an example looks like
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