Distillation transfers knowledge from a powerful teacher model into a lightweight student. Developers capture outputs from the larger model and fine-tune a smaller model to mimic its behaviour. This approach reduces latency and costs while keeping accuracy high.
OpenAI supports distillation with stored completions and evaluations. Teams still face challenges creating diverse datasets and tuning hyperparameters, but the method enables scalable deployment across resource constrained environments.