From Abandoned Prototype to AI-Powered Google Form Platform

A developer revived an abandoned prototype and built an AI-powered Google Form Generator, a full-stack web application that creates real Google Forms from natural language prompts using Google Gemini AI. The project, originally a proof of concept with a messy backend and incomplete features, was transformed into a stable, production-ready platform through a focus on usability, architecture, and reliability. Key improvements included backend refactoring and UI cleanup, with GitHub Copilot used as a productivity tool to accelerate development.

This is a submission for the GitHub Finish-Up-A-Thon Challenge I revived and completed my unfinished project: AI-Powered Google Form Generator — a full-stack web application that creates real Google Forms from natural language prompts using Google Gemini AI. The original idea started as a small experiment: “Can AI automatically generate a complete Google Form from a simple text description?” Initially, the project only supported basic prompt-to-form generation. It worked as a proof of concept, but the user experience was incomplete, the backend structure was messy, and several important features were missing. Eventually, I stopped working on it. For the GitHub Finish-Up-A-Thon Challenge, I decided to revisit the project and properly finish it by transforming it from a simple AI demo into a more complete workflow platform. The application now supports: https://github.com/dpkpaswan/AI-powered-Google-Form-Generator https://github.com/dpkpaswan/AI-powered-Google-Form-Generator “Create a college symposium registration form with participant details, department selection, workshop preferences, and feedback questions.” https://github.com/dpkpaswan/AI-powered-Google-Form-Generator https://github.com/dpkpaswan/AI-powered-Google-Form-Generator When I first started this project, it was mainly a proof of concept focused on AI-generated forms. The original version had multiple issues: Over time, I kept adding ideas without properly finishing the core workflow. The project slowly became harder to maintain and eventually got abandoned. For this challenge, I focused less on adding random new features and more on improving usability, architecture, reliability, and overall product quality. Major improvements I made during the revival process: One important lesson from this process was: Finishing and polishing a project is much harder than starting one. The biggest improvements were not flashy AI features — they were stability, usability, and better system structure. GitHub Copilot helped me significantly during the rebuilding and cleanup process. I mainly used Copilot for: One of the most useful parts was backend refactoring. The earlier version had tightly coupled logic, and Copilot helped accelerate the process of separating business logic into cleaner service layers. I also used Copilot while improving UI components and simplifying repetitive coding tasks during frontend cleanup. Instead of treating Copilot as a replacement for development, I used it as a productivity tool to speed up implementation and refactoring while still making the technical decisions manually. Some of the biggest technical challenges were: I also realized that overengineering can easily destroy project momentum. At one point, I was adding too many ideas without stabilizing the core product experience. This challenge helped me focus on actually finishing the application. This challenge pushed me to revisit an abandoned project and finally complete it properly. The biggest takeaway for me was: A polished and usable product matters more than endlessly adding features. There are still future improvements I want to make, but this challenge helped me transform an unfinished prototype into a much more stable and production-ready application. Thanks for reading This is a submission for the GitHub Finish-Up-A-Thon Challenge