AI Synthetic Data Tutorial: Self-Instruct, Distill

TL;DRSynthetic data — generated by models — augments or replaces scarce real data.

When real labeled data is scarce or sensitive, you can generate it: an LLM produces instruction/response pairs (self-instruct), or a larger model creates training data for a smaller one (a form of distillation). It scales cheaply and avoids privacy issues. Risks: synthetic data can amplify the generator's biases and errors, and training models only on model output can degrade quality ("model collapse") if unchecked.

Key points

Generate training data with models
Self-instruct; teacher → student data
Scales cheaply, sidesteps privacy
Risks: bias amplification, model collapse

Common mistakes

Training only on synthetic data unchecked
Ignoring inherited biases/errors
Assuming synthetic = as good as real

Try it: Name a benefit and a risk of training on model-generated data.

Example code

<!doctype html><html><head><meta charset="utf-8"></head>
<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>LLM generates (instruction, response) pairs
+ cheap, scalable, private
− inherits biases; all-synthetic → model collapse</pre></body></html>

Open the interactive lesson →

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