What Is Synthetic Data?
Synthetic data is artificially generated data that statistically mimics the properties and patterns of real-world data, created using AI models, simulations, or rule-based systems to augment or replace real training data.
How Synthetic Data Works
Real-world data for AI training is often scarce, expensive to collect, or restricted by privacy regulations. Synthetic data addresses these challenges by generating realistic but artificial datasets. LLMs can generate synthetic text data, GANs and diffusion models can create synthetic images, and simulation engines can produce synthetic sensor data. Synthetic data is used to augment existing datasets, create balanced training sets for underrepresented classes, and enable training without privacy concerns. However, quality matters — poorly generated synthetic data can introduce biases or artifacts. Many frontier AI models now use synthetic data as part of their training pipeline.
Real-World Examples
A self-driving car company generating millions of synthetic driving scenarios in a simulator to train perception models
A healthcare AI startup creating synthetic patient records to train models without accessing real patient data
OpenAI using GPT-4 to generate synthetic training data for fine-tuning smaller specialized models