Synthetic Data

In short

Data that’s artificially generated — often by AI itself — rather than collected from the real world.

Think of flight simulators. Pilots can’t learn everything from real flights alone — it would be too expensive, too dangerous, and you’d never cover enough rare scenarios. So they train in simulators that recreate realistic conditions without the real-world consequences. Synthetic data works kind of the same way for AI models — you generate realistic-looking data to train on without needing to go out and collect it all from scratch.

Sometimes you just don’t have enough real Data to train a model properly. Maybe the data is too expensive to collect, maybe it doesn’t exist in large enough quantities, or maybe there are serious privacy concerns — like with medical records or financial transactions. You can’t just hand over thousands of real patient files to train an AI. That’s where synthetic data comes in: you generate fake-but-realistic data that has the same statistical properties as the real thing, without exposing anyone’s private information.

Here’s where it gets interesting — LLMs are now commonly used to generate training data for other, smaller models. A powerful model like GPT-4 can produce thousands of question-answer pairs, conversations, or labeled examples that are then used to fine-tune a smaller, cheaper model. Companies like Meta and Google have openly used synthetic data generated by their larger models to train their next generation of systems. It’s become a pretty standard practice in the industry.

But there’s a real risk here, and it’s worth understanding. If the synthetic data has flaws — biases, inaccuracies, or patterns that don’t reflect reality — the model trained on it inherits all of those flaws. And it gets worse: if you keep training new models on outputs from previous models, errors compound over time. Researchers call this “model collapse” — the model gradually loses its ability to produce diverse, accurate outputs because it keeps learning from increasingly distorted versions of reality. Think of it like making a photocopy of a photocopy of a photocopy — each generation gets a little worse. The consensus right now is that synthetic data works best when it’s anchored in real, human-generated data, not used as a complete replacement for it. Data Quality matters just as much here — maybe even more, because the flaws are harder to spot when the data looks convincingly real.

Related

• Data - synthetic data is artificially generated data
• Data Quality - garbage in, garbage out applies even more with synthetic data
• Training - synthetic data is used to train models when real data is insufficient
• LLMs - large language models are often used to generate synthetic training data
• Fine-Tuning - synthetic data is commonly used to fine-tune smaller models