FEW-SHOT- PROMPTING

 Few-shot prompting is a way of using a large language model where you include a small number (typically 1–5) of input–output examples of the task in the prompt so the model infers the pattern and applies it to new cases, instead of relying only on an abstract instruction or separate training.

In translational research (e.g., applying NLP/LLMs to biomedical or clinical problems), few-shot prompting means embedding a handful of carefully chosen domain-specific exemplars—such as clinical note snippets with their labels, short patient vignettes with gold-standard summaries, or sentence pairs with expert translations—directly in the prompt so the model can “learn in context” how to perform that specialized task on new, real-world data

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