What is the difference between zero-shot and few-shot prompting?

Zero-shot gives the model an instruction with no examples; few-shot includes a handful of input→output examples that demonstrate the desired behaviour and format. Few-shot leverages in-context learning — the model imitates the pattern without any retraining. Use few-shot when you need a precise output format or the task is nuanced; use zero-shot for simple, common tasks to save tokens. Examples must be consistent and representative, because the model copies them faithfully, flaws included.