Few-Shot Prompting

Also known as: few-shot, in-context learning examples

Few-shot prompting includes a small number of worked input-output examples directly in the prompt to demonstrate the expected behavior before presenting the actual task. The examples act as implicit instructions that steer the model's output format, reasoning style, and domain vocabulary without any weight updates. Selecting diverse, representative examples and ordering them thoughtfully can dramatically improve accuracy on tasks where zero-shot instructions alone are insufficient.