Few-Shot Learning

Few-shot prompting provides multiple examples to establish patterns, handle variations, and improve consistency. It's one of the most powerful prompt engineering techniques.

What is Few-Shot?

Multiple examples establish the classification rules through pattern recognition.

Why Few-Shot Works

1. Pattern Recognition - AI identifies rules from examples
2. Disambiguation - Multiple examples clarify edge cases
3. Consistency - Output format stays uniform
4. Reduced Instructions - Show, don't tell

Optimal Number of Examples

Task Complexity	Recommended Examples
Simple	2-3
Moderate	3-5
Complex	5-8
Highly nuanced	8-12

More isn't always better - examples use tokens and can cause overfitting.

Exercise: Create Few-Shot Classification

Few-Shot Example Selection

Diversity

Cover different categories and edge cases:

Balance

Don't over-represent one category:

Difficulty Range

Include easy and harder cases:

Few-Shot for Format Consistency

The examples establish:

• Headline - Description format
• Specific benefit language
• Hyphen separator
• Action-oriented copy

Exercise: Few-Shot for Tone

Few-Shot Anti-Patterns

Inconsistent Formats

Too Similar Examples

Misleading Examples

Including incorrect or ambiguous classifications confuses the AI.

Few-Shot for Complex Tasks

For multi-step tasks, show the complete process:

Practice: Building Example Sets

Few-shot learning lets you teach the AI your specific requirements through demonstration rather than explanation.