synthetic instruction data

Hand-writing instruction examples is slow and expensive, so a now-standard shortcut is to let a capable model write the training data for a smaller or newer one. You prompt a strong LLM to generate instructions and answers, then fine-tune on its output.

Pipelines like Self-Instruct seed a few human examples, ask the model to brainstorm many more instructions, generate responses, and filter the results for quality and diversity. Synthetic data is cheap, scalable, and easy to aim at gaps in coverage. The dangers are real, though: the student inherits the teacher's mistakes and biases, errors can compound, and training a model too heavily on its own kind of output can degrade it. Filtering and a mix of real data are the usual guardrails.