AI Glossary · Last reviewed August 2026

Few-shot Learning

Hand-written by a real person. Reviewed against current practice in August 2026.

Definition

Showing a model two to five examples in the prompt so it follows the pattern, without any retraining.

Why it matters

Few-shot learning lets AI models learn new tasks from just a handful of examples instead of thousands. This is why you can show ChatGPT two examples of your writing style and it adapts immediately.

Tools that leverage few-shot learning well can be customized quickly without expensive fine-tuning, saving time and money for businesses.

How it works

3 steps

STEP 01

Provide examples in the prompt

You include 2-5 examples of the input-output pattern you want the model to follow.

STEP 02

Model recognizes the pattern

The model analyzes the examples and identifies the structure, tone, format, or logic you are demonstrating.

STEP 03

Apply to new input

The model applies the learned pattern to your actual task, producing output that matches the style of your examples.

Frequently asked questions

What is few-shot learning?+

Few-shot learning is a machine learning approach where an AI model learns to perform a task using only a small number of examples.

How does few-shot learning work?+

The model receives examples within a prompt and uses them as guidance to generate similar outputs without additional training.

What are the benefits of few-shot learning?+

It allows AI models to adapt quickly to new tasks with fewer examples and less training effort.

New to Few-shot Learning?

See the tools that use it.

The fastest way to understand Few-shot Learning is to see it inside real products. Browse hand-reviewed tools that put it to work, each one checked by a person before it was listed.

Browse hand-reviewed AI tools