Lab Walkthrough Your First API Call

You’ve reviewed the code — now run it yourself against a live model. This short hands-on lesson walks through executing a minimal Python program that calls an LLM via the OpenAI SDK using a real API key from your terminal. No simulations: your code, your credentials, and a real model responding.

A retro-style black banner with neon text that reads "YOUR FIRST API CALL" and the subtitle "Run it yourself — for real." A small green button below says "FOR REAL" and there's a "HANDS-ON LAB" label above.

Environment and prerequisites

Python 3.11 is already installed and your project’s virtual environment is active.
Install the official SDK:

pip install openai

It’s safer to keep your API key out of source files. Use an environment variable such as OPENAI_API_KEY and load it from os.environ instead of pasting the key in code.

Create and run the example

Create a file named hello_llm.py.
Paste the short program below, save the file, and run it with python3 hello_llm.py.

This example demonstrates the minimal flow:

Create an SDK client
Send a chat completion request
Print the returned message

from openai import OpenAI

# Step 1: Create a client (replace the string with your key or use an environment variable)
client = OpenAI(api_key="YOUR_API_KEY")

# Step 2: Send a message
response = client.chat.completions.create(
    model="gpt-4o",
    temperature=0.7,
    messages=[
        {"role": "system", "content": "You are a helpful assistant. Be concise."},
        {"role": "user", "content": "What is an LLM?"}
    ]
)

# Step 3: Print the response
print(response.choices[0].message.content)

Explanation of key steps

client creation: OpenAI(api_key="...") — creates an authenticated client instance.
model: selects which model to use (here "gpt-4o").
temperature: controls randomness (0.0 = deterministic, higher = more diverse).
messages: the chat-style conversation (system -> assistant behavior, user -> request).
response.choices[0].message.content: the assistant’s returned text.

Run it from your terminal:

root@controlplane ~/project via 🐍 v3.12.3 (venv) $ python3 hello_llm.py
An LLM (Large Language Model) is a neural network trained on massive text data to understand and generate human language.
root@controlplane ~/project via 🐍 v3.12.3 (venv) $

Parameters reference

Parameter	Purpose	Example
`model`	Which model to invoke	`gpt-4o`
`temperature`	Controls output randomness; `0.0` is deterministic	`0.7`
`messages`	Chat-style input array (system, user, assistant)	`[{ "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "What is an LLM?" }]`
`response` path	Where generated text appears	`response.choices[0].message.content`

Change the prompt and experiment with temperature

Modify the user message to:
- "Write a one-sentence story about a robot."
Set temperature to 0.0 and run the script twice. With 0.0 the model is deterministic (the same prompt returns the same output):

# After setting temperature=0.0
root@controlplane ~/project via 🐍 v3.12.3 (venv) $ python3 hello_llm.py
A lonely robot discovered a forgotten garden and learned to grow flowers.
root@controlplane ~/project via 🐍 v3.12.3 (venv) $ python3 hello_llm.py
A lonely robot discovered a forgotten garden and learned to grow flowers.

Set temperature to 1.0 and run multiple times to see more diverse outputs:

# After setting temperature=1.0
root@controlplane ~/project via 🐍 v3.12.3 (venv) $ python3 hello_llm.py
A lonely robot discovered a forgotten garden and learned to grow flowers.
root@controlplane ~/project via 🐍 v3.12.3 (venv) $ python3 hello_llm.py
A robot baked a soufflé on Neptune's rings while dreaming of electric sheep.

Summary: temperature controls randomness — 0.0 = deterministic (repeatable), higher values = more varied creativity. Security reminder

Do not commit API keys to source control. Use environment variables (for example, OPENAI_API_KEY) or a secrets manager and never paste keys into public repositories.

Use an environment variable (recommended) If you prefer to load the API key from the environment instead of hardcoding it, update your client creation like this:

import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

Set the environment variable in your shell before running:

export OPENAI_API_KEY="sk-..."
python3 hello_llm.py

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Lab Walkthrough Your First API Call

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