Few-shot learning leverages LLMs' ability to learn from examples in the prompt. By showing 2-5 examples of input-output pairs, you can teach the model new tasks without any fine-tuning.
Zero-shot = no examples (just instructions). One-shot = one example. Few-shot = 2-5 examples. This is one of the most surprising capabilities of large language models — they can generalize from very few demonstrations.