Few-Shot Prompting with Examples
Design few-shot prompts by selecting and formatting 2–5 representative examples to guide model behavior.
Few-shot prompting improves accuracy by including a small number of worked examples in your prompt. These examples demonstrate the task's input-output pattern, allowing the model to infer the desired behavior without fine-tuning. Few-shot is particularly effective for nuanced tasks, domain-specific language, or when you need consistent output formatting. The challenge is selecting representative examples that cover edge cases without overwhelming the context window. Quality of examples matters more than quantity: 2–5 well-chosen examples often outperform 10 mediocre ones. Few-shot bridges the gap between zero-shot simplicity and full fine-tuning complexity.
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