Define Few-Shot Learning?

Few-shot learning is when a handful of example input-output pairs are included directly in the prompt to demonstrate exactly what kind of response is wanted, before asking the model to handle a new case.

• Typically uses somewhere between two and a handful of examples
• Helps the model understand a specific format, tone, or task structure that a plain instruction alone might leave ambiguous
• Consumes more of the context window than zero-shot prompting, since the examples themselves take up tokens

Few-shot prompting is a practical, no-training-required way to steer a model toward a very specific style of response when a plain instruction isn't precise enough on its own.

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