What is Few-Shot Learning?

Few-shot learning provides examples in the prompt to guide LLM behavior. The model learns the pattern from examples and applies it to new inputs without any training.

Types of In-Context Learning

• Zero-shot: No examples, just instructions
• One-shot: Single example
• Few-shot: 2-5 examples typically
• Many-shot: More examples (uses more tokens)

Best Practices

• Use diverse, representative examples
• Match example format to desired output
• Order examples thoughtfully
• Balance token cost vs accuracy