MasterPrompting

Technique Guide

Few-Shot Prompting

Show the AI exactly what you want by providing examples. Few-shot prompting is one of the most reliable ways to get consistent, structured, on-format outputs — no fine-tuning required.

What is Few-Shot Prompting?

Few-shot prompting means including 2–5 input/output examples in your prompt to demonstrate the pattern you want the AI to follow. Instead of describing your requirements in words, you show them. The model infers the pattern from your examples and applies it to new inputs.

Contrast this with zero-shot prompting (no examples, just instructions) and one-shot prompting (a single example). Few-shot sits between them: enough examples to establish a clear pattern, but not so many that you waste context window tokens.

Few-shot example structure

Input: "The product broke after one use"
Sentiment: Negative

Input: "Exceeded my expectations, would buy again"
Sentiment: Positive

Input: "Decent quality for the price"
Sentiment: [AI fills this in]
Read: Zero-Shot vs Few-Shot — which should you use?

When to Use Few-Shot Prompting

Classification tasks

Sentiment, intent, category — show examples of each class.

Structured extraction

Pull specific fields from unstructured text in a consistent format.

Tone and style matching

Show examples of the writing style you want replicated.

Format consistency

When zero-shot ignores your format instructions, examples fix it.

Niche domains

Technical or specialized outputs where the model needs anchoring.

Translation patterns

Custom terminology, brand names, or non-standard conventions.

Articles

Related Lessons

Structured lessons covering few-shot prompting and the techniques that work alongside it.

Related Guides

Master Few-Shot Prompting

The Intermediate track covers few-shot, chain of thought, system prompts, and more in structured order.

Go to Few-Shot Lesson