Building an AI-Powered Trivia Game with GitHub Copilot

[cheeragpatel]

Building an AI-Powered Trivia Game with GitHub Copilot

Introduction

I recently embarked on an exciting project to create an AI-powered trivia game for our team. The project, now hosted on GitHub under the repository cheeragpatel/quiz-game, was inspired by the idea of using advanced AI models to make learning and team collaboration more engaging and fun.

Background

This idea came out of a team meeting for our holiday festivities. With a small team of 7 people and one of us coordinating a trivia game, would have meant leaving that member out of the fun. I thought it would be interesting to have an AI-powered trivia game that could generate questions and evaluate answers, allowing everyone to participate without needing a dedicated game master. This idea quickly gained traction, and I decided to take on the challenge of building the application.

Project Concept

The idea was simple yet challenging: develop a trivia game that leverages AI to generate and evaluate trivia questions. This application was intended to be a fun way for our team to test their knowledge on user-provided topics while showcasing the capabilities of AI in a practical application. My goal was to leverage GitHub Copilot as much as possible and use prompts to build the game with as little manual coding as possible.

Development Process

To bring this idea to life, I turned to GitHub Copilot Workspace. I leveraged Workspace to build a plan with Copilot's help on how to structure the application, including the game logic, user interface, and AI model integration. The development process was seamless, thanks to Copilot's ability to understand prompts and provide relevant code suggestions. Here's a brief overview of the steps I took to develop the application starting with a simple prompt:

1. Setting Up the Project: I started by creating a new repository and setting up the project structure. Copilot generated the initial codebase, including the necessary files and directories, to get us started.
   

2. Developing the Game Logic: Next, I focused on developing the game logic, including generating trivia questions, evaluating user responses, and keeping track of scores. Copilot helped us write the necessary functions and classes to handle these tasks effectively.
   

3. Integrating the AI Model: I then integrated an AI model, such as OpenAI's GPT-4 via GitHub Models to experiment, to generate trivia questions. Copilot provided the necessary code snippets to connect to the model and process the generated questions. This step was crucial in demonstrating the application's AI capabilities. Leveraging instructions from GitHub Models I was able to test the models see how they performed and what they were capable of, without the need to create accounts and setup OPenAI accounts. Once I was ready to integrate OpenAI, it was a a simple variable change as library is the same, making rapid testing and iteration possible.

4. Designing the User Interface: I then moved on to designing the user interface for the application. I prompted Copilot with the idea for how the game should look, feel, and operate in a browser window. Copilot generated the necessary HTML, CSS, and JavaScript code to create a simple, interactive interface for the game.
   

5. Once I had the basic outline, I moved directly from the workspace into a codespace to get a more robust IDE experience. My context with Copilot transferred over so I was able to continue my conversation with Copilot seamlessly. I was able to use the built-in terminal to run the application and test the game's functionality in real-time. This step was crucial in identifying and fixing any bugs or issues that arose during the development process.
   

Using Different GitHub Models

During the development process, I utilized different GitHub models to suit our needs:

• o1 Model: For parts of the project that required extensive reasoning, such as generating question logic and handling user inputs, I used the o1 model. This model's advanced capabilities allowed us to tackle complex problems effectively.
• o1-mini and gpt-4o Models: For smaller, incremental changes, I switched to the o1-mini and gpt-4o models. These models are perfect for making quick fixes and adjustments, ensuring a smooth development process.

Ease of Use and Speed

One of the most impressive aspects of using GitHub Copilot was the ease and speed with which I was able to develop the application. From concept to a working example, the entire process took only a few days. Copilot's ability to understand prompts and provide relevant code suggestions significantly accelerated the development timeline.

Results/Next steps

The team enjoyed the trivia game, and we received positive feedback on its functionality and user experience. The AI-generated questions added an element of surprise and challenge, making the game more engaging.
I plan to continue refining the game, adding more features specifically around scalability, and exploring new ways to enhance the user experience. The potential for AI in game development is vast, and I'm excited to see where this journey takes us.

Conclusion

Using GitHub Copilot to build our AI-powered trivia game was a remarkable experience. The ease of use, combined with the speed of development, made it an invaluable tool for our project. I encourage other developers to explore the capabilities of Copilot and see how it can transform their development workflows. Check out our repository, cheeragpatel/quiz-game, and give Copilot a try for your next project!

We would love to hear how GitHub Copilot has helped you in your projects. Share your experiences in the comments below!