Few-Shot Learning with Prompts: Providing Examples Within Prompts for Better Results

In this tutorial, you will learn about few-shot learning with prompts and how providing examples within prompts can lead to better AI responses. When I started working with AI models like GPT-4o, I often found that the responses were not as accurate as I wanted. Then I discovered that by including examples in my prompts, I could get much better results.

Introduction

Few-shot learning is a technique where you provide a few examples within your prompt to help the AI understand exactly what you're asking for. This is especially useful when you want the AI to generate responses in a specific format or style.

What is Few-Shot Learning?

In the context of prompt engineering, few-shot learning means giving the AI model a small number of examples to learn from within the prompt itself. This helps the model understand the pattern or format you expect in the response.

Practical Examples

Let's dive into some practical tech examples to see how this works.

Example 1: Generating Code Snippets

Suppose you want the AI to generate SQL queries based on natural language descriptions.

Prompt without Examples:

"Write an SQL query to select all users with age over 30."

AI Response:

SELECT * FROM users WHERE age > 30;

This works fine for simple queries, but if you have more complex requirements, the AI might not get it right.

Prompt with Few-Shot Learning:

"Convert the following descriptions into SQL queries:

Description: Get the names of all employees who work in the 'Sales' department.
SQL Query: SELECT name FROM employees WHERE department = 'Sales';

Description: Find the emails of customers who have made a purchase in the last 30 days.
SQL Query: SELECT email FROM customers WHERE purchase_date >= DATE_SUB(CURDATE(), INTERVAL 30 DAY);

Now, write an SQL query based on the following description:

Description: Retrieve the product names and prices of items that are in stock and cost less than $50.
SQL Query:"

AI Response:

SELECT product_name, price FROM products WHERE stock > 0 AND price < 50;

My Experience:

When I was building a small app that required dynamic SQL query generation, I struggled to get the AI to produce accurate queries. By providing examples within the prompt, the AI understood the pattern and generated correct queries consistently.

Example 2: Formatting Data Output

Suppose you want to convert JSON data into a specific format.

Prompt without Examples:

"Convert the following JSON data into a CSV format:

{
  "name": "Alice",
  "age": 30,
  "city": "New York"
}
"

AI Response:

It's uncertain what the AI might output.

Prompt with Few-Shot Learning:

"Convert the following JSON data into CSV format. Follow the examples below:

Example JSON:
{
  "name": "John",
  "age": 25,
  "city": "Los Angeles"
}
CSV Output:
name,age,city
John,25,Los Angeles

Example JSON:
{
  "name": "Emily",
  "age": 22,
  "city": "Chicago"
}
CSV Output:
name,age,city
Emily,22,Chicago

Now, convert this JSON data:

{
  "name": "Alice",
  "age": 30,
  "city": "New York"
}
CSV Output:"

AI Response:

name,age,city
Alice,30,New York

My Experience:

I was working on data migration and needed to convert JSON data into CSV format. By providing examples, the AI understood the exact format I needed and saved me a lot of time.

Example 3: Writing Documentation Comments

Suppose you have code functions and you want the AI to generate documentation comments.

Prompt with Few-Shot Learning:

"Add Python docstrings to the following functions. Follow the examples:

Example Function:

def add(a, b):
    return a + b

Docstring:

def add(a, b):
    '''
    Adds two numbers and returns the result.
    
    Parameters:
    a (int or float): The first number.
    b (int or float): The second number.
    
    Returns:
    int or float: The sum of a and b.
    '''
    return a + b

Example Function:

def multiply(a, b):
    return a * b

Docstring:

def multiply(a, b):
    '''
    Multiplies two numbers and returns the result.
    
    Parameters:
    a (int or float): The first number.
    b (int or float): The second number.
    
    Returns:
    int or float: The product of a and b.
    '''
    return a * b

Now, add a docstring to the following function:

def divide(a, b):
    return a / b

Docstring:"

AI Response:

def divide(a, b):
    '''
    Divides one number by another and returns the result.
    
    Parameters:
    a (int or float): The numerator.
    b (int or float): The denominator.
    
    Returns:
    float: The result of a divided by b.
    '''
    return a / b

My Experience:

When documenting my code, I found it tedious to write similar docstrings for multiple functions. By showing the AI a few examples, it was able to generate consistent and accurate docstrings for the rest of my functions.

Tips for Effective Few-Shot Learning

• Provide Clear Examples: Make sure your examples are clear and correctly formatted.

• Keep Examples Relevant: The examples should closely match the task you want the AI to perform.

• Limit the Number of Examples: Usually, 2-3 examples are sufficient. Too many examples might exceed the prompt length limit.

• Highlight Patterns: Ensure that the pattern you want the AI to follow is evident in your examples.

Potential Challenges

• Prompt Length Limitations: AI models have a limit on how much text they can process. Be mindful of this when adding examples.

• Overfitting to Examples: Sometimes, the AI might mimic the examples too closely. Make sure your examples are generic enough.

My Experience:

I once tried to include too many examples in a prompt, and the AI couldn't process it properly. I learned to keep it concise and only include the most relevant examples.

Wrapping Up

Few-shot learning with prompts is a powerful technique to get better results from AI models. By providing examples within your prompts, you can guide the AI to produce responses that are accurate and formatted the way you want.

I hope you find this guide helpful! Let me know if you have any questions or comments.

Related Articles

• Advanced Prompt Techniques: Using Instructions, Constraints, and Examples

• Key Elements of Effective Prompts: Clarity, Context, and Specificity in Prompt Design

• Using Questions vs. Statements: Different Prompt Formats and Their Effects