Few-shot learning is a technique where an LLM is given a small number of examples in the prompt to guide its behavior — enabling task-specific performance without fine-tuning the model.
WHY IT MATTERS
Few-shot learning leverages LLMs' in-context learning ability. By providing 2-5 examples of desired input-output pairs in the prompt, the model adapts its behavior to match the pattern. This is remarkably effective for formatting, classification, and structured output tasks.
The technique is particularly useful when: you need consistent output formats, the task has a clear pattern, and you don't have enough data or budget for fine-tuning.
For agent tool use, few-shot examples of successful tool calls help the model generate correct parameters and handle edge cases.
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FREQUENTLY ASKED QUESTIONS
How many examples is 'few-shot'?
Typically 2-5 examples. Zero-shot: no examples (just instructions). One-shot: one example. More examples generally improve consistency but consume context window tokens.
Does few-shot beat fine-tuning?
For many tasks, few-shot matches fine-tuned performance with zero training cost. Fine-tuning is better when: you have many examples, need production-level consistency, or want to reduce prompt size.
What makes a good few-shot example?
Representative of the task, clearly formatted, covers edge cases, and diverse (don't repeat the same pattern). Quality matters more than quantity.
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