In this talk, I'll show how large language models such as GPT-3 complement rather than replace existing machine learning workflows. Initial annotations are gathered from the OpenAI API via zero- or few-shot learning, and then corrected by a human decision maker using an annotation tool. The resulting annotations can then be used to train and evaluate models as normal. This process results in higher accuracy than can be achieved from the OpenAI API alone, with the added benefit that you'll own and control the model for runtime.