Few-Shot Prompting: Teaching AI Through Examples

Few-shot prompting is one of the most effective techniques for getting consistent, high-quality outputs from AI models. By showing the model 2-5 examples of what you want, you're essentially teaching it the task, format, and style in a single prompt. The results are often dramatically better than zero-shot prompts that rely solely on instructions.

Why Few-Shot Works

Language models learn patterns from examples. When you provide examples in your prompt, you're activating the model's pattern-matching capabilities. It sees the structure, style, and approach you want and replicates it. This is more reliable than trying to describe the desired output in words alone.

Research consistently shows that few-shot prompting improves performance across tasks. For format-specific outputs like JSON, code, or structured data, examples are often essential. For style matching, examples are the most reliable method.

Basic Structure

A few-shot prompt follows this pattern:

Example 1:
Input: [input]
Output: [output]

Example 2:
Input: [input]
Output: [output]

Example 3:
Input: [input]
Output: [output]

Now do the same for:
Input: [your input]

Choosing Good Examples

The quality of your examples determines the quality of outputs. Good examples should be:

• Representative: Cover the types of inputs you'll actually use
• Diverse: Show variation in inputs and outputs
• Clear: Unambiguous and easy to understand
• Correct: Demonstrate the exact approach you want

If your examples are too similar, the model may overfit to that specific pattern. If they're too different, the model may not find a consistent pattern. Aim for examples that represent the range of your use case while maintaining consistency in format and approach.

How Many Examples?

Typically, 2-5 examples work well. More examples can help for complex tasks, but there are diminishing returns. Very long prompts with many examples can also confuse the model or hit context limits.

Start with 2-3 examples. If results aren't consistent, add more. If results are good, you've found the right number. The optimal count depends on task complexity and model capabilities.

Common Use Cases

Format Matching

Few-shot is essential for getting specific formats like JSON, XML, or custom structures. Show examples of the exact format you need.

Style Transfer

Want content in a specific style? Show examples of that style. The model will pattern-match to replicate it.

Task Learning

For novel tasks the model hasn't seen, examples teach it what to do. This is more reliable than trying to explain the task.

Practical Example

Convert these product descriptions to marketing copy:

Example 1:
Product: Wireless headphones with noise cancellation
Marketing: Experience crystal-clear audio with our premium wireless headphones. Advanced noise cancellation technology blocks out distractions, letting you focus on what matters—your music, your calls, your moment.

Example 2:
Product: Ergonomic office chair with lumbar support
Marketing: Transform your workspace with our ergonomic office chair. Engineered with advanced lumbar support to keep you comfortable through long workdays, so you can focus on productivity, not discomfort.

Now convert:
Product: Smart fitness tracker with heart rate monitor

Tips for Success

Be consistent: Use the same format across all examples. Inconsistency confuses the model.

Show edge cases: Include examples that cover unusual inputs you might encounter.

Iterate: If outputs don't match your examples, refine the examples or add more.

Combine with instructions: Examples work best when combined with clear instructions about the task and any constraints.