Few-Shot Prompting Explained: Teach AI by Example
By Learnia Team
Few-Shot Prompting Explained: Teach AI by Example
This article is written in English. Our training modules are available in French.
What if you could teach an AI exactly what you want just by showing it a few examples? That's the power of few-shot prompting—one of the most practical techniques in prompt engineering.
What Is Few-Shot Prompting?
Few-shot prompting is a technique where you provide the AI with a small number of examples (the "shots") before asking it to complete a similar task.
Instead of explaining in words what you want, you demonstrate it.
The Core Idea
- →Zero-shot: No examples, just instructions
- →One-shot: One example provided
- →Few-shot: 2-5 examples provided
The more examples you give, the better the AI understands the pattern you want.
Why Does Few-Shot Prompting Work?
Large Language Models excel at pattern recognition. When you show them examples, they extract the underlying pattern and apply it to new inputs.
This is called in-context learning—the model learns from the examples you provide within the prompt itself, without any fine-tuning or retraining.
Key Benefits
- →Precision: Examples define exactly what you want
- →Consistency: Output format matches your examples
- →Flexibility: Works for almost any task
- →No training required: Learning happens at inference time
Zero-Shot vs. Few-Shot: A Comparison
Zero-Shot (No Examples)
Classify the sentiment of this review as positive, negative, or neutral:
"The product arrived late but works great."
→ AI might say: "Mixed" or "Positive" (inconsistent)
Few-Shot (With Examples)
Classify the sentiment of these reviews:
Review: "Absolutely love it! Best purchase ever."
Sentiment: positive
Review: "Terrible quality. Broke after one day."
Sentiment: negative
Review: "It's okay, nothing special."
Sentiment: neutral
Review: "The product arrived late but works great."
Sentiment:
→ AI says: "positive" (consistent with your format)
The examples teach the AI your classification style.
How to Structure Few-Shot Prompts
A good few-shot prompt has three parts:
1. Task Description (Optional)
A brief explanation of what you're doing.
2. Examples
Input-output pairs that demonstrate the pattern.
3. The Actual Query
The new input you want the AI to process.
Example Structure
[Optional: Brief task description]
Input: [Example 1 input]
Output: [Example 1 output]
Input: [Example 2 input]
Output: [Example 2 output]
Input: [Your actual query]
Output:
Best Practices for Few-Shot Prompting
1. Use Diverse Examples
Cover different cases to help the AI generalize:
Product: Laptop
Category: Electronics
Product: Running shoes
Category: Sports & Fitness
Product: Novel by Stephen King
Category: Books
Product: Wireless earbuds
Category:
2. Keep Examples Consistent
Use the same format for every example. Consistency is key.
3. Order Can Matter
For some tasks, putting similar examples together or ordering by difficulty can improve results.
4. Quality Over Quantity
3-5 well-chosen examples often outperform 10 mediocre ones.
Common Use Cases
Few-shot prompting excels at:
- →Classification tasks — sentiment, categories, labels
- →Format conversion — data transformation
- →Style matching — writing in a specific tone
- →Entity extraction — pulling structured data from text
- →Translation patterns — custom terminology
Limitations to Know
Few-shot prompting isn't magic:
- →Token limits: Examples consume context window space
- →Example bias: Bad examples lead to bad outputs
- →Complex reasoning: Some tasks need more than pattern matching
- →Diminishing returns: After 5-10 examples, gains flatten
Key Takeaways
- →Few-shot prompting teaches AI by example, not explanation
- →Provide 2-5 diverse, high-quality examples
- →Keep example format consistent
- →Works best for pattern-based tasks like classification and formatting
- →Quality of examples matters more than quantity
Ready to Master Prompt Structure?
This article covered the what and why of few-shot prompting. But crafting the perfect examples requires understanding how LLMs process prompts.
In our Module 1 — LLM Anatomy & Prompt Structure, you'll learn:
- →How LLMs actually process your prompts
- →Advanced prompt anatomy: roles, delimiters, and structure
- →When to use zero-shot vs. few-shot vs. many-shot
- →Hands-on exercises crafting effective example sets
- →Common pitfalls and how to avoid them
Module 1 — LLM Anatomy & Prompt Structure
Understand how LLMs work and construct clear, reusable prompts.