Synthetic Data
Algorithmically generated data that replicates the statistical properties of real data, used to train, evaluate, and test AI systems when real data is scarce, expensive, or sensitive.
What it is
Synthetic data is information generated by algorithms — not captured from the real world — that replicates the statistical properties and structure of real data. In the AI context, it is used to train models, build evaluation benchmarks, and test systems when real data is insufficient, expensive to obtain, or contains sensitive information.
The practice has become central to modern LLM development. Models like DeepSeek-R1 and the reasoning families from OpenAI and Anthropic use synthetic data extensively during post-training, either as fine-tuning data or as judgments generated by evaluator models.
Generation methods
Model distillation
A large, capable model generates data used to train a smaller model. The "teacher" model produces high-quality responses that the "student" model learns to replicate.
Instruction-based generation
An LLM is asked to generate examples following specific instructions:
Generate 50 questions about Kubernetes with their answers.
Each question should cover a different difficulty level.
Include real production scenarios.
Data augmentation
Transforming existing data to create variations: paraphrasing, translation, format changes, numerical value perturbation.
Adversarial generation
Creating data designed to expose model weaknesses: edge cases, adversarial prompts, security scenarios. This connects directly to AI safety practices.
Use cases
| Case | Problem | Synthetic data solution |
|---|---|---|
| Pre-launch evaluation | No real users yet | Generate representative queries and scenarios |
| Privacy | Data contains PII | Generate data with the same statistical properties without real information |
| Scarce data | Domain with few examples | Augment the dataset with generated variations |
| Red teaming | Test model robustness | Automatically generate adversarial prompts |
| Post-training | Improve specific capabilities | Generate high-quality instruction-response pairs |
Risks and limitations
- Model collapse: recursively training models on synthetic data can degrade quality generation after generation
- Amplified bias: synthetic data inherits and can amplify the biases of the generating model
- Lack of diversity: generated data tends to be less diverse than real-world data
- Validation required: quality metrics are needed to verify that synthetic data is representative
Quality metrics
- Statistical fidelity: distribution similarity between real and synthetic data
- Utility: performance of a model trained on synthetic vs. real data
- Privacy: guarantee that real data doesn't leak into synthetic data
- Diversity: coverage of the space of possible inputs
Why it matters
As AI systems move from prototypes to production, the quality of training and evaluation data becomes the bottleneck. Real-world data is expensive, slow to collect, and often restricted by privacy regulations. Synthetic data breaks this dependency: teams can iterate on evaluation suites in hours instead of weeks, test edge cases that rarely occur naturally, and build datasets for domains where real data simply doesn't exist yet.
References
- A Deep Dive Into the Role of Synthetic Data in Post-Training — Analysis of synthetic data use in LLM post-training, 2025.
- Evaluating Language Models as Synthetic Data Generators — Evaluation of LLMs as synthetic data generators, 2024.
- Best Practices and Lessons Learned on Synthetic Data — Liu et al., 2024. Best practices for synthetic data generation.