Resume Project Overview
The resume project is an AI-powered resume builder focused on helping users turn rough career notes into polished, ATS-friendly resumes with a fast editing workflow and clean exports. Instead of treating AI as a separate chatbot, the product keeps AI assistance close to the editing surface so users can generate, refine, and export resumes inside one guided workflow.
The Challenge
The app is designed for students and professionals who need to create role-tailored resumes quickly without fighting clunky forms or generic templates. The product goal is to turn a messy brag sheet into structured resume content, help users tailor phrasing for specific roles, preserve fast editing with reliable saved state, and export polished resumes without layout issues.
Technical Approach
The project is structured as a frontend-first application with a guided resume workflow. Users add raw experience, achievements, and career context, the app generates structured resume content with AI, users refine sections directly inside the builder, and the final output is exported as a polished resume. Firestore handles persistence across sessions and devices, the builder emphasizes direct editing in the preview, and export-aware rendering keeps empty sections visible during editing while hiding them during export.
Outcome
The project demonstrates a practical use of generative AI inside a real product workflow. Its strongest engineering decisions are around keeping the editing experience fast, making persistence reliable, and ensuring that the same structured resume data can move cleanly from generation to editing to export.
What I Built & Learned
I learned how to balance responsive drafting with durable saved state, how to keep AI assistance close to the editing surface, and how to treat export quality as a first-class concern. The project also strengthened my approach to separating editing convenience from final document quality so both can coexist in the same workflow.
