How to Use Google Colab with Google’s LLM (Gemini) — A Beginner’s Guide

How to Use Google Colab with Google’s LLM (Gemini) — A Beginner’s Guide

1. Introduction

If you’ve been hearing about ChatGPT, Gemini, Claude, and other AI models, you’ve probably encountered the term LLM — short for Large Language Model.

In simple words:

An LLM is an AI model trained on vast amounts of text to understand and generate human-like language.
You can ask it questions, translate text, write code, summarize documents, and more.

Google’s latest LLM, Gemini, can be accessed right inside Google Colab — a free, browser-based notebook environment where you can run Python code without installing anything on your computer.

In this article, we’ll walk through:

• What an LLM is (without the jargon)
• How to set up Google Colab
• How to connect to Gemini using an API key
• How to run your first AI-powered script
• How LLMs can help in software testing

2. What is an LLM?

An LLM is:

• Large → trained on massive datasets (billions of words).
• Language → processes and understands human language.
• Model → a mathematical system that predicts the next word or idea.

Examples:

• OpenAI → ChatGPT models
• Google DeepMind → Gemini models
• Anthropic → Claude models

They work by predicting the next most likely piece of text — but when scaled up and fine-tuned, they can write essays, debug code, translate languages, and even answer complex reasoning questions.

3. Why Google Colab?

Google Colab is perfect for LLM beginners:

• No installation required
• Free cloud computing
• Easy to share and collaborate
• Built-in Python environment

All you need is a Google account.

4. Getting Started with Google Colab and Gemini

Step 1: Open Google Colab

Go to https://colab.research.google.com and create a new notebook.

Step 2: Get a Gemini API Key

1. Go to Google AI Studio.
2. Click Get API key.
3. Copy the key — you’ll use it in your notebook.

Step 3: Store Your API Key in Colab Secrets

1. In Colab, click the 🔑 “Secrets” icon on the left sidebar.
2. Add a new secret:

• Name: GOOGLE_API_KEY
• Value: (paste your Gemini API key)

Save.

Step 4: Install the Gemini SDK

!pip install -q google-genai

Step 5: Load Your API Key

from google.colab import userdata
import os

# Load API key from Colab Secrets
os.environ["GOOGLE_API_KEY"] = userdata.get('GOOGLE_API_KEY')

Step 6: Run Your First Gemini Script

from google import genai

client = genai.Client()

english_text = "Hello, how are you?"

response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents=f'Translate the following English text to Hindi: "{english_text}"'
)

print(response.text)

Step 7: Example Output

**नमस्ते, आप कैसे हैं?** (Namaste, aap kaise hain?)

You can run the code from my github branch — https://github.com/sleepingfreak94/llmtest/blob/main/Translate.ipynb
Just add you secret key

5. How LLMs Can Help with Software Testing

LLMs like Gemini aren’t just for writing blog posts — they can be a powerful ally in software testing.

Here’s how they can help:

5.1 Test Case Generation

Instead of manually writing dozens of test cases, you can describe your software feature and let Gemini generate:

• Positive tests (expected behavior)
• Negative tests (unexpected inputs)
• Edge cases (boundary conditions)

Example prompt in Colab:

feature_description = """
A login system where the user must enter a username and password.
Passwords must be at least 8 characters, with a number and a special character.
"""
response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents=f"Generate a list of functional and edge case test cases for the following feature: {feature_description}"
)
print(response.text)

5.2 Code Review and Bug Detection

Paste a code snippet into Gemini and ask it to:

• Identify potential bugs
• Suggest improvements
• Check for security vulnerabilities

5.3 Test Automation Script Writing

Gemini can help you:

• Generate unit test code for Python (using pytest or unittest)
• Write API testing scripts for tools like Postman or requests
• Suggest UI testing scripts for Selenium or Playwright

5.4 Documentation for Test Plans

You can feed Gemini your test steps, and it will:

• Format them neatly
• Add missing details
• Create professional documentation for QA teams

6. What’s Next?

Once you’ve got Gemini running in Colab, you can:

• Summarize PDFs
• Generate blog posts
• Create interactive chatbots
• Build AI-powered testing tools

7. Final Thoughts

LLMs like Gemini are changing how we work, learn, and test software.
With Google Colab, you can experiment for free, without worrying about setup headaches.

Start small — try generating test cases, reviewing code, or automating repetitive testing tasks.
Then, integrate Gemini into your CI/CD pipeline for even more efficiency.

💡 Pro Tip: Keep your API key secret! Never share it in public notebooks or GitHub repos.

If you liked this guide, consider sharing it with friends or colleagues who want to start with AI and LLMs — especially in software testing.