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Giving a model a handful of worked examples inside the prompt so it can infer the pattern before answering.
Few-shot prompting places several input–output examples in the context so the model learns the task in-context, without any weight updates. Brown et al. (2020) showed this ability emerges with scale, and that adding a few examples often closes much of the gap to fine-tuned systems for common tasks.
Examples are most useful when the desired format is specific or unusual — exactly the cases where zero-shot tends to wobble.
Primary, peer-reviewed and archival sources for this definition.
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