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Few-Shot Prompts

Few-shot prompting is a technique in which an AI model is given multiple examples before being asked to perform a similar task. The examples help the model understand the expected pattern, format, style, or relationship between the input and output.

In this chapter, you will learn what few-shot prompts are, how they work, when to use them, their advantages and limitations, and how to create effective few-shot prompts.

What Is a Few-Shot Prompt?

A few-shot prompt provides multiple examples that demonstrate how the AI model should perform a particular task.

For example:

Classify the following sentences as Formal or Informal.

Example 1:
Sentence: Please provide the requested information.
Classification: Formal

Example 2:
Sentence: Can you send me the details?
Classification: Informal

Example 3:
Sentence: I would appreciate your assistance.
Classification: Formal

Now classify:
Sentence: Could you please share the report?

The examples show the AI model how different sentences should be classified. The model can use this pattern to classify the new sentence.

How Do Few-Shot Prompts Work?

A few-shot prompt generally contains an instruction, multiple examples, and a new input that the AI model needs to process.

Instruction
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Example 1
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Example 2
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Example 3
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New Input
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AI Model
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Generated Output

The examples provide additional guidance about the expected behavior of the AI model.

For example:

Task:
Convert technical language into simple English.

Example 1:
Technical: The system facilitates data synchronization.
Simple: The system keeps data synchronized.

Example 2:
Technical: The application utilizes an authentication mechanism.
Simple: The application uses a login system.

Now simplify:
Technical: The software implements an automated backup mechanism.

The AI model can identify the pattern from the examples and apply it to the new sentence.

Simple Example of a Few-Shot Prompt

A simple few-shot prompt can be used for text classification.

Classify each sentence as Positive or Negative.

Example 1:
"The product works perfectly."
Classification: Positive

Example 2:
"The application crashes frequently."
Classification: Negative

Example 3:
"The customer support team was very helpful."
Classification: Positive

Now classify:
"The software is difficult to use."

The multiple examples demonstrate the classification pattern before the new input is provided.

Few-Shot Classification Prompts

Few-shot prompting is useful for classification tasks where examples can demonstrate how information should be categorized.

For example:

Classify each customer review as Positive, Negative, or Neutral.

Example 1:
Review: "The product is excellent and easy to use."
Classification: Positive

Example 2:
Review: "The product arrived late and was damaged."
Classification: Negative

Example 3:
Review: "The product is acceptable but nothing special."
Classification: Neutral

Now classify:
Review: "The product works well, but the battery life is short."

The examples provide a reference for the three possible categories.

Few-Shot Sentiment Analysis

Few-shot prompts can be used to identify the sentiment expressed in text.

For example:

Identify the sentiment as Positive, Negative, or Neutral.

Example 1:
Text: "I really enjoyed using this application."
Sentiment: Positive

Example 2:
Text: "The application is slow and unreliable."
Sentiment: Negative

Example 3:
Text: "The application provides basic features."
Sentiment: Neutral

Now analyze:
Text: "The application is useful and easy to navigate."

The examples help establish how different sentiments should be identified.

Few-Shot Text Transformation

Few-shot prompting can demonstrate how text should be rewritten, simplified, shortened, or transformed.

For example:

Rewrite technical sentences using simple English.

Example 1:
Technical: The system facilitates automated data processing.
Simple: The system processes data automatically.

Example 2:
Technical: The application utilizes a secure authentication mechanism.
Simple: The application uses a secure login system.

Example 3:
Technical: The software provides an interface for data visualization.
Simple: The software provides a way to display data visually.

Now simplify:
Technical: The platform implements an automated reporting mechanism.

The examples demonstrate the expected style and level of simplification.

Few-Shot Translation Prompts

Few-shot prompts can also be used to demonstrate a particular translation style or format.

For example:

Translate the following English sentences into Spanish.

Example 1:
English: Welcome to our website.
Spanish: Bienvenido a nuestro sitio web.

Example 2:
English: Thank you for your support.
Spanish: Gracias por su apoyo.

Example 3:
English: Please contact us for more information.
Spanish: Por favor, contáctenos para obtener más información.

Now translate:
English: Learn more about our services.

The examples show the expected translation format.

Few-Shot Summarization Prompts

Few-shot prompts can show the AI model how information should be summarized.

For example:

Summarize each paragraph in one sentence.

Example 1:
Text: Python is a popular programming language used for
web development, automation, data science, and AI.
Summary: Python is a versatile programming language used
in many technical fields.

Example 2:
Text: Cloud computing allows organizations to access
computing resources through the internet.
Summary: Cloud computing provides internet-based access
to computing resources.

Now summarize:
Text: Machine learning enables computers to identify
patterns in data and make predictions.

The examples demonstrate the expected length and style of the summaries.

Few-Shot Code Generation Prompts

Few-shot prompting can help when code needs to follow a particular structure or style.

For example:

Create Python functions based on the examples.

Example 1:
Task: Calculate the square of a number.
Code:
def square(n):
    return n * n

Example 2:
Task: Calculate the double of a number.
Code:
def double(n):
    return n * 2

Now create a function that calculates the cube of a number.

The examples provide a coding pattern that the AI model can follow.

Few-Shot Data Extraction

Few-shot prompting can demonstrate how specific information should be extracted from text.

For example:

Extract the product name and price.

Example 1:
Text: The laptop is available for $800.
Product: Laptop
Price: $800

Example 2:
Text: The smartphone costs $500.
Product: Smartphone
Price: $500

Example 3:
Text: The tablet is priced at $300.
Product: Tablet
Price: $300

Now extract information from:
Text: The wireless keyboard costs $75.

The examples show exactly which information should be extracted and how it should be formatted.

Few-Shot Structured Output

Few-shot prompting can be useful when the AI model needs to generate output in a specific structure.

For example:

Convert the information into the following format.

Example 1:
Input: Python is a programming language used for data science.
Output:
Name: Python
Type: Programming Language
Use: Data Science

Example 2:
Input: MySQL is a database system used to store and manage data.
Output:
Name: MySQL
Type: Database System
Use: Data Management

Now convert:
Input: JavaScript is a programming language commonly used for web development.

The examples demonstrate the expected output structure.

Few-Shot vs Zero-Shot vs One-Shot Prompts

The main difference between these prompting techniques is the number of examples provided to the AI model.

Prompt Type	Number of Examples	Basic Idea
Zero-Shot	0	The model performs the task based only on instructions.
One-Shot	1	The model uses one example to understand the expected pattern.
Few-Shot	Multiple	The model uses several examples to understand the task and expected output.

When to Use Few-Shot Prompts?

Few-shot prompting is particularly useful when the desired output is difficult to describe using instructions alone.

Common applications include:

• Text classification
• Sentiment analysis
• Text transformation
• Data extraction
• Translation
• Summarization
• Content formatting
• Code generation
• Structured output generation
• Pattern recognition

Advantages of Few-Shot Prompts

Few-shot prompting provides several advantages.

• Provides clear examples: Multiple examples show the model exactly how the task should be performed.
• Improves consistency: Examples can help the model follow a particular pattern.
• Useful for complex patterns: Several examples can explain patterns that are difficult to describe with words alone.
• Supports custom formats: Examples can demonstrate specialized output structures.
• Reduces ambiguity: Multiple examples can clarify how different inputs should be handled.

Limitations of Few-Shot Prompts

Few-shot prompting also has some limitations.

• Long prompts can consume more input tokens.
• Creating useful examples can require additional time.
• Poor-quality examples can lead to poor results.
• Too many examples can make the prompt unnecessarily large.
• The model may focus on irrelevant details in the examples.
• Examples may not cover every possible situation.

How to Choose Good Examples

The quality and relevance of examples are important in few-shot prompting.

Choose examples that:

• Clearly demonstrate the task.
• Closely match the actual inputs.
• Represent different possible cases.
• Contain accurate outputs.
• Use a consistent format.
• Do not contain unnecessary information.

Use Different Examples When Necessary

When a task has multiple possible categories or patterns, include examples that represent those differences.

For example:

Classify the message as Question, Complaint, or Request.

Example 1:
Message: How can I reset my password?
Classification: Question

Example 2:
Message: My account has been locked for two days.
Classification: Complaint

Example 3:
Message: Please update my email address.
Classification: Request

Now classify:
Message: Can you help me change my account password?

The examples cover each available category and help the model distinguish between them.

Keep Examples Consistent

Examples should follow a consistent structure so that the AI model can easily identify the relationship between input and output.

For example:

Example 1:
Input: Python
Output: Programming Language

Example 2:
Input: MySQL
Output: Database System

Example 3:
Input: HTML
Output: Markup Language

Input: JavaScript

Using the same labels and format makes the pattern easier to understand.

Best Practices for Few-Shot Prompts

Follow these practices when creating few-shot prompts:

• Clearly define the task.
• Use relevant and accurate examples.
• Include examples representing different cases.
• Keep the examples consistent.
• Use concise examples when possible.
• Show both input and expected output.
• Avoid unnecessary information in examples.
• Use enough examples to demonstrate the pattern.
• Avoid adding examples that do not contribute to the task.
• Test the prompt with new inputs.
• Review and refine the examples when necessary.

Example of an Effective Few-Shot Prompt

The following prompt uses multiple examples to teach the AI model a specific transformation pattern:

Act as a technical editor.

Rewrite technical sentences using simple English.

Example 1:
Original: The system facilitates automated data processing.
Rewritten: The system processes data automatically.

Example 2:
Original: The application utilizes an authentication mechanism.
Rewritten: The application uses a login system.

Example 3:
Original: The platform implements an automated reporting mechanism.
Rewritten: The platform creates reports automatically.

Now rewrite:
Original: The software provides an interface for
managing user authentication.

This is a few-shot prompt because multiple examples are provided before the new input.

Few-Shot Prompting Workflow

A basic few-shot prompting workflow can be represented as follows:

Define the Task
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Select Relevant Examples
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Format the Examples
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Add the New Input
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Generate the Response
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Review the Output
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Refine the Examples or Instructions

This process can be repeated to improve the consistency and usefulness of the generated responses.