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Synthetic Data Generation

Using one LLM to generate training data, eval data, or augmentation examples for another model or app: labeled question-answer pairs, edge cases, role-played conversations.

What Is Synthetic Data Generation?

Common uses: bootstrapping an eval set when you have no labeled data, generating edge-case prompts to test guardrails, creating multi-turn conversations to fine-tune a smaller model, or augmenting rare-class examples for classification. Tools: distilabel, GPT-4 + structured prompts, Claude for harder reasoning generation.

How Groovy Web Uses This

We bootstrap client eval sets with GPT-4 or Claude when no production data exists, then layer real user data on top once the app ships.

Related Terms

Fine-Tuning (AI) LLM Evals Model Training

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