Few-Shot Prompting

Few-shot prompting shows the model what you want through examples: instead of describing the task in the abstract, you include two, three, or five sample inputs and corresponding ideal outputs in the prompt, then give it the real input. This takes advantage of the model’s ability to recognize patterns — after seeing several examples, it’s much more likely to follow the format, style, or reasoning approach you demonstrated. Few-shot prompting is especially useful when the task is hard to describe precisely in words but easy to show by example, such as a specific output format or a nuanced classification task. The tradeoff is that it consumes context window space and may not generalize well beyond the specific pattern of the examples provided.