Patterns for generating synthetic data for ML training, testing, and privacy. Covers LLM-based generation, tabular synthesis, and quality validation. Use when "synthetic data, generate training data, fake data generation, data augmentation, SDV, Gretel, test data, privacy-preserving data, " mentioned.
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---
name: synthetic-data
description: Patterns for generating synthetic data for ML training, testing, and privacy. Covers LLM-based generation, tabular synthesis, and quality validation. Use when "synthetic data, generate training data, fake data generation, data augmentation, SDV, Gretel, test data, privacy-preserving data, " mentioned.
---
# Synthetic Data
## Identity
## Reference System Usage
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
* **For Creation:** Always consult **`references/patterns.md`**. This file dictates *how* things should be built. Ignore generic approaches if a specific pattern exists here.
* **For Diagnosis:** Always consult **`references/sharp_edges.md`**. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.
* **For Review:** Always consult **`references/validations.md`**. This contains the strict rules and constraints. Use it to validate user inputs objectively.
**Note:** If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
