Exploring Few-Shot Prompting with Azure OpenAI Prompt Engineering in Python

Welcome to this article where we will delve into the concept of few-shot prompting using Azure OpenAI Prompt Engineering in Python.

Understanding Few-Shot Prompting

Few-shot prompting is a technique that allows us to include exemplars within prompts, providing guidance to the model and improving its performance.

Let’s look at an example:

Sentiment analysis is a great example of few-shot prompting.

• Positive Sentiment: Text: “I love this new phone. It’s amazing!”
• Negative Sentiment: Text: “This restaurant had terrible food and awful service.”

Advantages of Few-Shot Prompting:

1. Data Efficiency: Few-shot prompting allows models to make accurate predictions or generate content with minimal labeled training data, which reduces the need for large datasets.
2. Flexibility: It enables models to generalize across different tasks and domains, making them adaptable to various applications.
3. Customization: Few-shot prompts can be tailored to specific tasks, providing guidance and context to generate desired outputs.
4. Ease of Use: It simplifies the interaction with AI models, making them accessible to users without technical expertise.

Disadvantages of Few-Shot Prompting:

1. Limited Context: Few-shot prompts may not provide enough context for complex tasks, leading to potential inaccuracies in responses.
2. Challenges in Fine-tuning: Training models for few-shot learning can be complex and time-consuming, especially for custom or domain-specific tasks.
3. Dependency on Prompting: The model’s performance heavily relies on the quality and relevance of the provided prompts, which may require human input.
4. Scalability: Implementing few-shot learning for multiple tasks or languages can be resource-intensive and may not always scale effectively.

In essence, while few-shot prompting is data-efficient and flexible, it’s important to consider its limitations and design carefully when applying it to different AI tasks.

Comparison between Zero-Shot and Few-Shot Prompting

Zero-Shot Prompting:

In zero-shot prompting, the model is given minimal or no specific information about the task.

The model is expected to generate responses without any task-specific training data, relying on its pre-existing knowledge for generalization.

Few-Shot Prompting:

In few-shot prompting, the model is provided with a small amount of task-specific information or examples as part of the prompt.

This information guides the model and provides context for performing a particular task.

Implementing Few-Shot Prompting in Python

Objective: Perform sentiment analysis on the given text using the following example as a reference.

• Positive Sentiment: Text: “I love this new phone. It’s amazing!”
• Negative Sentiment: Text: “This restaurant had terrible food and awful service.”

Prompt: Celebrating a well-deserved victory!!!

Expected Response: Positive

Now, let’s dive into the code and write the program.

# import the necessary libraries
import os
import requests
import json
import openai

from dotenv import load_dotenv

# Load the .env file
load_dotenv()

# Set the OpenAI API key and endpoint
openai.api_key = os.getenv("AZURE_OPEN_KEY")
openai.api_base = os.getenv("AZURE_END_POINT") 
openai.api_type = "azure"
openai.api_version = '2023-07-01-preview' 

deployment_name = os.getenv("DEPLOYMENT_NAME")

# Set the system role and user message variables
system_role = """Find text sentiment in given text:
: I love this new phone. It's amazing!!!
positive
: I hate this new phone. It's the worst.
negative"""

user_message = f"""
I love this new phone.It's amazing!!!"""

# Send a completion call to generate the sentiment analysis
response = openai.ChatCompletion.create(
    engine = os.getenv("DEPLOYMENT_NAME"),
    messages = [
        {"role": "system", "content": system_role},
        {"role": "user", "content": user_message},        
    ]
)

# Print the sentiment analysis output
print(response['choices'][0]['message']['content'])

In the next article, we will explore additional prompt engineering techniques.