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few-shot-learning

Teach models new tasks from a handful of examples — prompting strategies, example selection, and in-context learning mechanics.

skills/few-shot-learning/SKILL.md

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## Overview

Few-shot learning with large language models means teaching a task through examples
placed in the prompt — no weight updates. Show the model 2–10 input-output pairs
demonstrating the pattern, then give it a new input. The model infers the task
from the demonstrations: the output format, the label space, the reasoning style.
It's the fastest way to prototype a new capability, and for many tasks it matches
or beats fine-tuning, especially when labeled data is scarce.

What makes it work is still partly mysterious, but the practical levers are well
mapped: example selection (which demonstrations), example ordering (sequence
effects are real), formatting (consistent delimiters the model can latch onto),
and the number of shots (more isn't always better — context gets diluted). Modern
practice treats the few-shot prompt as a small program to be engineered and