OpenAI's GPT 5.6 rollout now requires US government approval on a "customer by customer basis"

Introduction

In this tutorial, we'll explore how to work with large language models (LLMs) like those developed by OpenAI, even as the regulatory landscape evolves. While the recent news about GPT-5.6 requiring government approval highlights the increasing scrutiny of AI development, we can still learn how to interact with these powerful models using simple Python code. This tutorial will teach you how to set up an environment to work with OpenAI's API, make requests to language models, and understand the basic concepts behind how these systems work.

Prerequisites

Before starting this tutorial, you'll need:

• A computer with internet access
• Basic knowledge of Python programming (variables, functions, and modules)
• An OpenAI API key (you'll need to sign up for an account at platform.openai.com)
• Python 3.6 or higher installed on your system

Step-by-Step Instructions

1. Install Required Python Packages

First, we need to install the OpenAI Python library that will allow us to communicate with the API. Open your terminal or command prompt and run:

pip install openai

This command installs the official OpenAI Python client library, which provides an easy way to interact with OpenAI's services like GPT models.

2. Set Up Your API Key

Before making any API calls, you need to configure your API key. You can do this in a few ways:

1. Set it as an environment variable
2. Hardcode it in your Python script (not recommended for security reasons)

For this tutorial, we'll use the environment variable approach. Set your API key in your terminal like this:

export OPENAI_API_KEY='your_api_key_here'

On Windows, use:

set OPENAI_API_KEY=your_api_key_here

Replace 'your_api_key_here' with your actual API key from the OpenAI platform.

3. Create Your Python Script

Now create a new Python file (e.g., ai_tutorial.py) and add the following code:

import openai

# Initialize the OpenAI client
openai.api_key = "your_api_key_here"

# Define a simple function to interact with the model
def ask_question(prompt):
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": prompt}
        ],
        max_tokens=150,
        temperature=0.7
    )
    return response.choices[0].message['content'].strip()

# Test the function
if __name__ == "__main__":
    question = "What is artificial intelligence?"
    answer = ask_question(question)
    print(f"Question: {question}")
    print(f"Answer: {answer}")

This script sets up a basic interaction with OpenAI's API using the GPT-3.5 model. The function ask_question takes a prompt, sends it to the model, and returns the generated response.

4. Run Your Script

Save your script and run it using:

python ai_tutorial.py

You should see output like:

Question: What is artificial intelligence?
Answer: Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. These systems can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

This demonstrates how you can interact with AI models programmatically. Even though the news mentions new models requiring government approval, you can still experiment with existing models like GPT-3.5.

5. Experiment with Different Prompts

Try changing the prompt in the script to see how the model responds to different questions:

question = "Explain quantum computing in simple terms"
# or
question = "What are the benefits of renewable energy?"
# or
question = "Write a short poem about technology"

Each prompt will generate a different response from the model, showing how flexible and powerful these systems can be.

6. Understand Model Parameters

In the code, we used two important parameters:

• max_tokens: Controls how long the response can be
• temperature: Controls randomness in responses (0.0 is very focused, 1.0 is more creative)

Try changing these values to see how they affect the output:

response = openai.ChatCompletion.create(
    model="gpt-3.5-turbo",
    messages=[...],
    max_tokens=200,  # Increase response length
    temperature=0.9   # Make responses more creative
)

Summary

In this tutorial, we've learned how to set up a basic environment for working with OpenAI's language models. We installed the required Python library, configured our API key, and created a simple script that can send prompts to AI models and receive responses. While the recent news about GPT-5.6 rollout shows increasing government oversight of AI development, this tutorial demonstrates that developers can still experiment with existing models like GPT-3.5. Understanding these basics is crucial for anyone interested in AI development, as it forms the foundation for more advanced applications like chatbots, content generation, and automated analysis.

Remember that as AI regulations evolve, staying informed about changes in model availability and access requirements is important. This tutorial gives you the starting point to begin working with AI models today, even as the landscape continues to change.