Few-Shot Prompting
Showing, Not Just Telling
Discover how providing concrete examples dramatically reduces guesswork and improves output quality.
What You'll Learn
- • Zero-shot vs few-shot prompting
- • Selecting effective examples
- • Pattern recognition in action
- • Building example libraries
Zero-Shot vs Few-Shot Prompting
Zero-shot prompting means asking the AI to do something without showing it any examples. You are relying entirely on the AI's training data to understand what you want.
Few-shot prompting means providing 2-3 examples of the exact output you want. The AI recognises the pattern and replicates it with your new input.
Why Few-Shot Works
LLMs excel at pattern recognition. When you show them examples, they identify the pattern (structure, style, approach) and apply it to your new request. This is far more reliable than describing what you want in words.
Selecting Effective Examples
Your examples should be:
- Representative: They should cover the range of inputs you will provide
- High Quality: They should be exactly the kind of output you want
- Diverse: Show variety in inputs while maintaining consistency in output style
- Clear: The pattern should be obvious and easy to recognise
Few-Shot Example Structure
Example 1:
Input: [Your first example input]
Output: [Your first example output]
Example 2:
Input: [Your second example input]
Output: [Your second example output]
Example 3:
Input: [Your third example input]
Output: [Your third example output]
Now, for this new input: [Your actual request]
Output:Pattern Recognition in Action
The AI does not just copy your examples. It identifies the underlying pattern: the structure, the style, the approach, the level of detail, the tone. Then it applies that pattern to your new input.
This is why few-shot prompting is so powerful: you are showing, not just telling. The AI sees exactly what good output looks like.
Building Example Libraries
Create reusable example libraries for common tasks:
- Email templates with examples
- Blog post structures with examples
- Report formats with examples
- Code patterns with examples
Save your best examples. They become reusable assets that improve every prompt you write.
Completing the Four Pillars
Congratulations! You have now mastered all four pillars of effective prompting: Persona, Context, Output Requirements, and Few-Shot Examples. These four techniques can improve your AI results by approximately 80%. Next, you will learn advanced strategies that take your prompting to the next level.