How I Built ThinkAI for my client in 3 Weeks Using lovable_dev + cursor_ai

I didn’t write a single line of code for this MVP.

Here’s the exact process I followed to build it, FAST and efficiently.

1. Start With a Solid Project Brief

Before writing any code, clarity is key.

I started by defining:
- What’s the product? A web app for students to actively engage with study materials.
- Who’s it for? University students and lifelong learners.
- What problem does it solve? Helps users retain complex topics through AI-generated quizzes, summaries, and feedback.

Prompt:
"I’m building a web app called ThinkAI, an AI-powered study assistant that helps students retain information better through quizzes and summaries. Could you help me draft a structured project brief for this MVP?"

A clear brief ensures every decision aligns with the end goal.

2. Generate Features Using AI

Once the problem was clear, I turned to ChatGPT to define the feature set.

Prompt:
"Here’s the project brief: [Insert Brief]. Generate a list of key features and technical requirements for building this MVP."

The core features identified for ThinkAI:

- Manual Progress Tracking: Users input page numbers or timestamps to sync study progress.
- On-Demand Quizzes: AI generates quizzes based on what the user is studying.
- Scheduled Quizzes: Periodic reminders prompt users to test their knowledge.
- AI Summaries: Quick study summaries generated from recent material.
- Personalized Feedback: Performance-based recommendations for improvement.

This gave me a structured foundation to work with.

3. Draft the PRD

Now, I needed to organize and prioritize the features into a Product Requirements Document (PRD).

I used the MoSCoW framework to categorize features:

- Must-Have: Manual tracking, quizzes, summaries, feedback.
- Should-Have: Adaptive quiz difficulty (planned for later).
- Could-Have: AI-powered conversational explanations (future scope).
- Won’t-Have: Automated video analysis (planned for phase 2).

Prompt:
"Here’s the feature list: [Insert feature list]. Use the MoSCoW framework to categorize features based on their importance for the MVP, ensuring a fast and focused release."

A clear PRD ensures I build only what’s essential for launch.

4. Prepare Essential Documentation

Before starting development, have these key documents ready:

- Project Requirements Doc: Defines MVP scope, features, and objectives
- Tech Stack Doc: Outlines frameworks and technologies
- App Flow Doc: Maps the entire user journey
- Frontend Guidelines Doc: Ensures UI consistency
- Backend Structure Doc: Sets up API routes, DB schemas, and core logic

Save these as .md files in both Cursor and Lovable root directories. Keeping everything organized from the start makes collaboration smoother and accelerates development.

5. Map Out the Screens and Structure

Next, I mapped out the core screens needed for Thinkai.

Prompt:
"Based on this PRD and core features, list all the screens required for the MVP."

ThinkAI needed:
- Landing Page – Introduces the product.
- Signup/Login – User authentication.
- Study Dashboard – Tracks study progress.
- Quiz Interface – Generates AI-powered questions.
- Summary Page – Displays AI-generated study summaries.

With this roadmap in place, I moved to development.

6. Building 70-80% of the MVP in Lovable

I used Lovable to handle the foundation.

This covered:
- UI design for the study dashboard.
- Authentication with Supabase.
- Database setup for tracking study progress and storing quiz results.

Lovable eliminated boilerplate work and let me focus on functionality.

7. GitHub Integration

Lovable automatically created a GitHub repo for the project, keeping all changes version-controlled.

This made it seamless to sync the project into Cursor for adding advanced logic.

8. Supabase for Backend Setup

For authentication, data storage, and API connections, I used Supabase.

I set up:
- User signups and logins with email authentication.
- A database to store study progress, quizzes, and AI-generated summaries.
- API endpoints to retrieve study recommendations.

Prompt:
"Set up Supabase authentication using phone authentication as the provider."

9. AI-Powered Quizzes and Summaries

The core value of ThinkAI comes from AI-generated quizzes and summaries.

Using OpenAI’s API, I implemented:
- On-Demand Quizzes: Users can test themselves at any time.
- Scheduled Quizzes: A reminder prompts them at set intervals.
- Summaries: AI generates key takeaways based on study material.

Prompt:
"Integrate OpenAI’s API to generate quizzes based on study material input and provide AI summaries on demand."

This turned ThinkAI into an active study tool rather than just a passive note-taker.

10. Moving to Cursor for Advanced Logic

Once the core app was built in Lovable, I moved to Cursor for deeper refinements.

Here, I implemented:

- Study session tracking: Users manually input page numbers or timestamps.
- Quiz performance analysis: The system tracks correct/incorrect answers and suggests weak areas.
- Personalized recommendations: Users get study suggestions based on quiz results.

Prompt:
"Optimize the study progress tracking feature and implement quiz performance analysis with targeted recommendations."

Cursor’s AI-assisted development made debugging and refining these features much faster.

11. Final Steps and Deployment

In three weeks, the MVP was fully functional with:

- Study synchronization for PDFs.
- AI-powered quizzes and summaries.
- Personalized feedback for better learning.
- A structured, distraction-free study experience.

Video analysis will be included in phase 2 after gathering market feedback.

12. What This MVP Can Do

This is what you can build with @lovable_dev + @cursor_ai , without writing a single line of code.

The client’s vision? An AI-powered study companion designed to help students study smarter, with features like:

- On-demand quizzes
- Scheduled quizzes at set intervals
- Page and session summaries
- Personalized feedback

The best part? It’s built on OpenAI’s GPT-4o, meaning the platform evolves as the model improves. That’s the advantage of AI-driven web apps.

Next up: Adding video support, but that’s planned for phase 2 after gathering market feedback.