ModelRefs / Few-Shot Prompting — AI Glossary

Few-Shot Prompting — AI Glossary

Providing a small number of example input-output pairs in the prompt to demonstrate the desired behavior to the model.

Overview

Few-shot prompting works because LLMs perform in-context learning. 3–8 examples is typical; more rarely helps and consumes context. Example selection matters — diverse, representative examples outperform random sampling.

Reference details

Topic	prompting
Last reviewed	2026-06-24

Example: Examples teach the edge cases, not the task

For sentiment labelling, the useful examples are not three clear positives. They are the ones that pin down your conventions: sarcasm, mixed sentiment, and a review that is about delivery rather than the product. Examples are where you encode the decisions a definition cannot express.

Commonly confused with

In-context learning does not update the model. The examples influence one response and are gone; nothing is retained between calls. That is the difference from fine-tuning, and the reason few-shot costs tokens on every single request.

When to use it

Reach for it when:

The desired output form is easier to show than to describe
There are conventions and edge cases a definition would not capture
You need behaviour changeable without retraining

Reach for something else when:

Examples would consume context you need for actual content
The pattern is stable and high-volume — fine-tuning amortises better
Your examples are unrepresentative; the model will copy the skew faithfully

Referenced by

This term is used by the following ModelRefs references:

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