Few-Shot Learning

Few-Shot Learning is a prompting technique where you provide a language model with a small number of examples demonstrating the desired task or output format. The model then generalizes from these examples to handle new inputs in the same way, without requiring any fine-tuning or retraining.

This approach leverages the in-context learning capabilities of large language models. For instance, showing 3-5 examples of translating formal text to casual text, then providing a new formal sentence, often produces good casual translations without explicit instructions.

Few-shot learning is powerful because it allows rapid prototyping and customization without model training. Best practices include: using diverse, representative examples, formatting examples consistently, ordering examples thoughtfully, and including edge cases. When examples become too numerous or context length is limited, consider fine-tuning or RAG-based approaches instead.