Few-shot prompting is a technique used to improve the performance of language models by providing them with a small number of examples or demonstrations within the input prompt. It guides the model to understand the task better, offering context on how to respond or generate the desired output. This approach bridges the gap between zero-shot learning, where no examples are provided, and fine-tuning, which involves excessive retraining. In practice, it helps models excel in tasks where a few systemic indications of desired outcomes make a significant difference, like translating text or classifying sentiment.