StudyBuddy AI: Taming Gemma 2B to Build a Local Quiz Generator for a Friend

Who I Built It For

My friend is currently preparing for university exams and technical interviews (focusing on databases and C#). Reading dry documentation gets boring quickly, so I wanted to build an interactive, multiple-choice quiz partner that tests their knowledge on any given topic. Since it's for a student, it had to be completely free and accessible without internet restrictions.

What I Built

I built StudyBuddy AI — a lightweight, completely local web application that generates 3-question multiple-choice quizzes on any subject.

GitHub Repository: https://github.com/Shadow16Ua/StudyBuddy-AI

The Tech Stack:

• AI Model: Google's gemma2:2b running locally via Ollama.
• Backend: .NET 10 Minimal API (C#) to handle prompting and JSON parsing.
• Frontend: Pure HTML, CSS, and Vanilla JavaScript (with a sleek dark mode).

📸 Demo

Why Open Innovation Matters Here

Choosing an open-weight model like Gemma 2B over a closed API (like OpenAI) was crucial for this project for three main reasons:

1. Zero Cost & Privacy: My friend can generate hundreds of quizzes without worrying about API limits, subscription fees, or sending their study data to a third-party server.
2. 100% Offline Capability: It runs perfectly on a standard laptop CPU, meaning they can study during commutes or internet outages.
3. The Engineering Challenge (Taming the 2B Model): This was the most interesting part. I quickly realized that small 2B models struggle to output complex JSON arrays (like 3 questions at once). It would constantly hallucinate structures or merge answers. Because I had full control over the local inference, I was able to completely redesign the backend pipeline. Instead of asking for 3 questions in one prompt, my C# backend asynchronously asks Gemma for one question, exactly 3 times in a loop, and then manually constructs a bulletproof JSON array. This guarantees perfect UI rendering every single time. I also shifted the "shuffle options" logic to the frontend to prevent the AI from confusing correct/incorrect indexes.

Open source allowed me to iterate rapidly, observe the raw output locally, and engineer a robust wrapper around a lightweight model to make it perform like a much heavier one.

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Note: I'm submitting this project for the Best Use of Gemma category as well!