Architecting Learntail: Building an AI Quiz Generator in Weeks

ArjanCodes////3 min read

Overview

is an AI-powered quiz generator that transforms text, URLs, and even YouTube videos into interactive assessments. The goal isn't just to build a tool, but to demonstrate an architectural blueprint that allows a single developer to go from concept to production in just a few weeks. By prioritizing simplicity and choosing a monolithic architecture over complex microservices, you can focus on the core value proposition of your AI application without getting bogged down in infrastructure overhead.

Prerequisites

To get the most out of this architecture, you should have a solid grasp of for backend development and for the frontend. Familiarity with for containerization and basic cloud deployment concepts on (GCP) is essential. You also need an understanding of how to interact with RESTful APIs.

Key Libraries & Tools

  • : A modern, high-performance web framework for building APIs with Python.
  • : The React-based framework used for the frontend to handle server-side rendering and routing.
  • : A fully managed NoSQL database-as-a-service.
  • : Powering the quiz generation via the GPT-3.5 Turbo model.
  • : The orchestration library used to manage prompts and AI interactions.
  • : A utility-first CSS framework for rapid UI development.
Architecting Learntail: Building an AI Quiz Generator in Weeks
Here Is a SIMPLE Architecture for Your AI Tool

Code Walkthrough & Repository Structure

We organize everything into a single monorepo. This drastically simplifies dependency management and CI/CD orchestration.

Backend Structure (FastAPI)

The backend is segmented by concern rather than service. The routers/ folder handles API endpoints like /quizzes and /users, while the database/ layer contains the logic for interacting with .

# backend/main.py snippet
from fastapi import FastAPI
from .routers import quizzes, auth

app = FastAPI()
app.include_router(quizzes.router)
app.include_router(auth.router)

Frontend Logic (Next.js)

The frontend uses dynamic routing to fetch quizzes based on a unique slug. This slug is stored in the database and retrieved via the API when a user visits a specific quiz URL.

// src/app/quiz/[slug]/page.tsx
async function QuizPage({ params }: { params: { slug: string } }) {
  const quiz = await getQuizFromAPI(params.slug);
  return <QuizPlayer data={quiz} />;
}

Deployment & CI/CD

We use to automate the build and deployment to . To avoid unnecessary costs, the workflow uses path filtering. If you only change code in the /backend folder, the frontend container does not rebuild. This keeps your CI/CD pipeline lean and efficient.

Tips & Gotchas

Avoid reinventing the wheel. Use existing libraries for everything from rate limiting to email verification. A major mistake developers make is trying to host their own database or AI models too early. Use "ready-to-go" services like for magic links and for payments. This allows you to launch fast and iterate based on real user feedback rather than technical assumptions.

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Architecting Learntail: Building an AI Quiz Generator in Weeks

Here Is a SIMPLE Architecture for Your AI Tool

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ArjanCodes // 15:47

On this channel, I post videos about programming and software design to help you take your coding skills to the next level. I'm an entrepreneur and a university lecturer in computer science, with more than 20 years of experience in software development and design. If you're a software developer and you want to improve your development skills, and learn more about programming in general, make sure to subscribe for helpful videos. I post a video here every Friday. If you have any suggestion for a topic you'd like me to cover, just leave a comment on any of my videos and I'll take it under consideration. Thanks for watching!

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