Few-shot
Prompting a model with a few examples to demonstrate the desired output.
Few-shot prompting places a few input-output examples inside the prompt, giving a foundation model a concrete pattern to follow before the real task. Because the examples live in the context window rather than the model weights, no retraining or fine-tuning occurs — the model generalizes from the demonstrations at inference time. Contrast it with zero-shot prompting, which gives no examples, and fine-tuning, which permanently adjusts weights using a labeled dataset. In Amazon Bedrock, few-shot examples improve format consistency and domain vocabulary without a fine-tuning job’s cost or latency. Watch out: too many examples can exceed token limits.
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