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

> Source: <https://dev.to/shadow16ua/studybuddy-ai-taming-gemma-2b-to-build-a-local-quiz-generator-for-a-friend-4mpo>
> Published: 2026-10-04 14:39:41+00:00

## 
  
  
  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](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.

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