🟢 TECHNICIAN TRACK • FOUNDATION

Tutorial #0: OpenAI API Setup

Configure Your AI Workbench in 20 Minutes

✅ CORE MISSION OF THIS TUTORIAL

By the end of this tutorial, the reader will be able to:

✅ Create an OpenAI account and get your API key
✅ Securely configure environment variables for API access
✅ Install the OpenAI Python package
✅ Make your first successful API test call
✅ Understand API costs, rate limits, and usage monitoring

This tutorial prepares your development environment for all Technician Track AI experiments.

⚠️

⚠️ SAFETY BOUNDARY REMINDER

This tutorial performs analysis only.

It must never be connected to:

Live PLCs
Production deployment pipelines
Safety-rated controllers
Motion or power systems

> All outputs are advisory-only and always require explicit human approval before any real-world action.

🌍 VENDOR-AGNOSTIC ENGINEERING NOTE

This tutorial uses:

▸ OpenAI API (GPT-4, GPT-4o, GPT-4o-mini models)
▸ Works on Windows, macOS, Linux
▸ Python 3.8+ required
▸ No automation hardware needed
▸ Pay-as-you-go pricing model

Future tutorials will show how to use other AI providers (Anthropic, local models), but OpenAI is the standard starting point.

ℹ️ About Our API Approach

Technician Track uses the OpenAI Python SDK v1.x with straightforward synchronous calls. This is the current stable, production-ready approach used by thousands of companies.

We're intentionally keeping the API patterns simple so you can focus on agentic concepts rather than API complexity.

What's next: Developer Track (Track 2) will introduce advanced patterns like structured outputs, async operations, streaming responses, and the new OpenAI Responses API. For now, master the fundamentals.

Estimated Time

20 min

Estimated Cost

~$0.01

Difficulty

Beginner

📋 Prerequisites

Before starting this tutorial, you should have:

• Python 3.8+ installed (from Tutorial T0: Python Readiness)
• A valid email address
• A credit/debit card for API billing setup

1️⃣ Create Your OpenAI Account

Step-by-Step Instructions

1.

Go to https://platform.openai.com/signup

This is the OpenAI Platform (for developers), not ChatGPT
2. Sign up with your email or Google/Microsoft account
3. Verify your email address
4.

Verify your phone number (required for API access)

This prevents abuse and is required for all API users

Note: New accounts receive $5 in free credits valid for 3 months. This is enough for all Technician Track tutorials.

2️⃣ Set Up Billing (Required)

Add Payment Method

1. Navigate to Settings → Billing
2. Click "Add payment method"
3. Enter your credit/debit card information
4.

Set a monthly usage limit (recommended: $10-20 for learning)

This prevents accidental overspending during experiments

Important: Even with free credits, you must add a payment method to access the API. Your card will only be charged after free credits are exhausted.

3️⃣ Generate Your API Key

Creating Your First API Key

1. Go to API Keys in the left sidebar
2. Click "Create new secret key"
3.

Give it a descriptive name (e.g., "technician-track-learning")
4.

COPY THE KEY IMMEDIATELY — you cannot view it again!

It will look like: sk-proj-abc123...

⚠️ Critical Security Warning

Never do this:

❌ Commit API keys to Git repositories
❌ Share keys in Discord, Slack, or public forums
❌ Hardcode keys in Python files like api_key = "sk-..."
❌ Email keys or store them in plain text files

If a key is exposed:

1. Revoke it immediately in the OpenAI dashboard
2. Generate a new key
3. Check your usage logs for unauthorized activity

4️⃣ Configure Your API Key (3 Methods)

You have three options for storing your API key. We'll show all three, but Method A is strongly recommended for security.

Quick Comparison

Method	Security	Setup	Use Case
A: System Environment	🟢 Most Secure	One-time setup	Production & learning
B: .env File	🟡 Good (if Git-ignored)	Per-project	Development projects
C: Hardcoded	🔴 Insecure	Immediate	Quick tests only

✅ Method A: System Environment Variables (RECOMMENDED)

Store your API key in your operating system's environment. This is the most secure method and works across all your Python projects.

🪟 Windows

Open PowerShell and run:

[System.Environment]::SetEnvironmentVariable('OPENAI_API_KEY', 'sk-proj-YOUR_KEY_HERE', 'User')

Restart your terminal after setting

🍎 macOS

Add to ~/.zshrc:

export OPENAI_API_KEY='sk-proj-YOUR_KEY_HERE'

Run: source ~/.zshrc

🐧 Linux

Add to ~/.bashrc:

export OPENAI_API_KEY='sk-proj-YOUR_KEY_HERE'

Run: source ~/.bashrc

Verification: After setting the variable, verify it's set correctly:


# Windows PowerShell
 echo $env:OPENAI_API_KEY


# macOS/Linux
 echo $OPENAI_API_KEY

Python usage:

Python

from openai import OpenAI

# Automatically reads OPENAI_API_KEY from environment
client = OpenAI()

🟡 Method B: .env File with python-dotenv

Store your API key in a .env file in your project directory. Good for managing different keys across multiple projects.

Step 1: Install python-dotenv

BASH

# Install python-dotenv package
pip install python-dotenv

Step 2: Create a .env file in your project folder

.env

# .env
OPENAI_API_KEY=sk-proj-YOUR_KEY_HERE

Step 3: Load it in your Python script

Python

from dotenv import load_dotenv
from openai import OpenAI

# Load environment variables from .env file
load_dotenv()

# Now OpenAI() can read OPENAI_API_KEY
client = OpenAI()

⚠️ Important: Never commit your .env file to Git.

🔴 Method C: Hardcoded in Script (NOT RECOMMENDED)

⚠️ SECURITY WARNING: This method is insecure and should ONLY be used for:

• Quick throwaway tests
• Scripts that will NEVER be committed to Git
• Learning exercises in a private, local environment

You can pass the API key directly to the OpenAI client, but this is dangerous if you accidentally commit the code or share it.

Python

from openai import OpenAI

# ❌ NOT RECOMMENDED - Easy to leak!
client = OpenAI(api_key="sk-proj-YOUR_ACTUAL_KEY_HERE")

Why this is risky:

• Easy to accidentally commit to Git
• Visible in code reviews and screen shares
• Can't easily rotate keys without editing code
• No separation between dev/test/prod environments

Which Method Should You Use?

For this tutorial series: Use Method A (System Environment Variables). It's the most secure, works everywhere, and is industry standard.

For larger projects later: Method B (.env files) becomes useful when managing multiple API keys or different environments.

Method C: Only use for throwaway test scripts. Never for production or shared code.

5️⃣ Install OpenAI Python Package

Install the official OpenAI Python library:

Terminal Command

pip install openai

Verify installation:

pip show openai

Expected output: You should see version 1.x.x or higher. As of December 2024, the latest version is 1.55.3.

6️⃣ Make Your First API Call

🧪 Experiment 1: Test API Connection

Objective

Verify that your API key works and you can successfully call the OpenAI API.

Python Code

Python

from openai import OpenAI

# Client automatically reads OPENAI_API_KEY from environment
client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {
            "role": "user",
            "content": "Respond with exactly: 'API connection successful.'"
        }
    ],
    max_tokens=10
)

print(response.choices[0].message.content)

Expected Output

API connection successful.

7️⃣ Understanding API Costs & Usage

Pricing by Model (December 2024)

Model	Input	Output	Use Case
`gpt-4o-mini`	$0.15 / 1M tokens	$0.60 / 1M tokens	Testing, learning, simple tasks
`gpt-4o`	$2.50 / 1M tokens	$10.00 / 1M tokens	Production, complex reasoning
`gpt-4-turbo`	$10.00 / 1M tokens	$30.00 / 1M tokens	Advanced tasks, high quality

What is a Token?

Roughly:

• 1 token ≈ 4 characters
• 100 tokens ≈ 75 words
• 1000 tokens ≈ 750 words

A typical tutorial experiment uses 500-2000 tokens (~$0.001-0.005 with gpt-4o-mini)

Monitoring Usage

Track your spending:

1. Go to Usage in dashboard
2. View daily breakdown by model
3. Set up usage alerts
4. Review monthly totals

All Technician Track tutorials cost <$5 total if using gpt-4o-mini

Cost Optimization Tip: Always start with gpt-4o-mini for learning. Only upgrade to gpt-4o or gpt-4-turbo when you need higher quality outputs.

8️⃣ Common Issues & Troubleshooting

❌ Error: "Invalid API key"

Causes:

• Environment variable not set correctly
• Typo when copying the API key
• Key was revoked in dashboard

Fix: Verify your environment variable is set. Regenerate the key if needed.

❌ Error: "You exceeded your current quota"

Causes:

• Free credits exhausted
• Monthly usage limit reached
• Payment method declined

Fix: Check billing dashboard. Add credits or update payment method.

❌ Error: "Rate limit exceeded"

Causes:

• Too many requests in short time
• Free tier rate limits (3 RPM, 200 RPD)

Fix: Wait 60 seconds and retry. Add payment method for higher limits.

❌ ModuleNotFoundError: No module named 'openai'

Causes:

• Package not installed
• Wrong Python environment

Fix: Run pip install openai. Check you're using the correct Python interpreter.

9️⃣ Official Documentation & Resources

For more detailed information about the OpenAI API, consult the official documentation:

🧭 Engineering Posture

This tutorial reinforced:

✓ Security first — API keys in environment variables, never in code
✓ Cost awareness — Understanding pricing and setting limits
✓ Testing before production — Start with mini models, verify setup
✓ Monitoring usage — Track spending and rate limits
✓ Documentation literacy — Know where to find official resources

🔑

API Setup Complete!

You're now ready to start building AI-powered industrial automation tools.

Next Tutorial

Tutorial #1: Agentic vs Automation →

Tutorial #0: OpenAI API Setup

✅ CORE MISSION OF THIS TUTORIAL

⚠️ SAFETY BOUNDARY REMINDER

🌍 VENDOR-AGNOSTIC ENGINEERING NOTE

ℹ️ About Our API Approach

📋 Prerequisites

1️⃣ Create Your OpenAI Account

Step-by-Step Instructions

2️⃣ Set Up Billing (Required)

Add Payment Method

3️⃣ Generate Your API Key

Creating Your First API Key

⚠️ Critical Security Warning

4️⃣ Configure Your API Key (3 Methods)

Quick Comparison

✅ Method A: System Environment Variables (RECOMMENDED)

🪟 Windows

🍎 macOS

🐧 Linux

🟡 Method B: .env File with python-dotenv

🔴 Method C: Hardcoded in Script (NOT RECOMMENDED)

Which Method Should You Use?

5️⃣ Install OpenAI Python Package

6️⃣ Make Your First API Call

🧪 Experiment 1: Test API Connection

Objective

Python Code

Expected Output

7️⃣ Understanding API Costs & Usage

Pricing by Model (December 2024)

What is a Token?

Monitoring Usage

8️⃣ Common Issues & Troubleshooting

❌ Error: "Invalid API key"

❌ Error: "You exceeded your current quota"

❌ Error: "Rate limit exceeded"

❌ ModuleNotFoundError: No module named 'openai'

9️⃣ Official Documentation & Resources

📚 OpenAI Platform Documentation ↗

🤖 Chat Completions Guide ↗

⚙️ API Reference ↗

📊 Usage & Rate Limits ↗

🧭 Engineering Posture

API Setup Complete!