Synthetic Data

Simple Definition

Synthetic data is fake data, generated artificially rather than collected from real sources. In AI, this means creating training examples, conversations, images, or other inputs that look realistic but were made by a machine (often by another AI model) rather than by humans or real events.

Why Synthetic Data Is Used

Real-world data has problems:

Privacy: real data about people can expose sensitive information
Scarcity: some types of data are rare (edge cases, unusual events, rare languages)
Bias: real data can reflect historical inequalities
Cost: collecting and labeling real data is expensive and slow
Licensing: you may not have rights to use real data for training

Synthetic data sidesteps many of these problems by letting you generate as much perfectly labeled data as you need.

How It’s Used in AI

Training language models: AI labs use one model (a “teacher” model) to generate examples that a smaller or different model (the “student”) learns from. This is how models like Meta’s Llama and many smaller models are trained, partly on outputs from frontier models.

Fine-tuning: Instead of hand-labeling thousands of examples, you can generate synthetic question-and-answer pairs, instruction-following examples, or conversations for training.

Testing and evaluation: Synthetic data lets you create specific edge cases or failure scenarios to test how a model handles difficult inputs.

Image generation: AI vision models can be trained on synthetic images, rendered scenes, diagrams, simulated environments, rather than only real photographs.

The Risks

Synthetic data has a key limitation: if the data generator is biased or wrong, the model learns those errors. There’s also the concern of “model collapse”, if AI models are primarily trained on AI-generated data, they may gradually drift from reflecting real human knowledge and language.

Most responsible AI development combines real data with synthetic data, rather than replacing one with the other entirely.