Synthetic Data

What is synthetic data?

In short, it's data that an AI makes up. The model first learns the patterns in real data, then mass-produces fresh examples with the same structure and distribution — but with no real people or events inside. It's like training a face generator on real photos, then producing endless faces that never existed.

Why would you want fake data?

Real data is expensive and hard to get
Medical images, financial transactions, rare-disease cases — they're scarce or locked behind privacy laws.
Compliance risk is real
Train on real user data and a leak becomes a disaster. Synthetic data carries no real person's info, so it sidesteps much of that.
You can generate exactly what's missing
Need to train a model to spot accidents on rainy days but have no samples? Generate thousands of them.

How is it made?

A common route: have a model learn the distribution of real data, then use a generative model — a diffusion model or a GAN — to mass-produce new samples. You can also have a large model “write” synthetic text by following rules, then use it to train a smaller model.

The catch

However real it looks, synthetic data is copied patterns from real data. If the source is biased, the synthetic data amplifies that bias. Lean on it too hard and a model can “train itself in circles”, learning its own made-up hallucinations.

Bottom line: synthetic data is AI-generated fake data — it saves you the privacy headache but inherits the risk of “training on make-believe”.