ResumeGPT
Overview
A modern web application that lets you create, edit, and optimize professional resumes using natural language prompts and AI-powered suggestions. Just describe your experience or role, and ResumeGPT helps you turn it into a polished resume. Choose from multiple templates, live preview your changes, and export your resume as a PDF in seconds. Now, you can also check how ATS-friendly your resume is with real-time scoring and optimization tips.
Technical Implementation
AI Integration with Gemini 2.0 Flash
Integrated Google's latest Gemini 2.0 Flash API for natural language processing, converting conversational input into structured resume data with intelligent content suggestions, ATS optimization, and professional formatting guidelines.
Advanced ATS Analysis with RAG & NLP
Implemented advanced ATS compatibility analysis using Retrieval-Augmented Generation (RAG) and Natural Language Processing (NLP). Features semantic skill extraction, bidirectional skill matching, TF-IDF-based keyword importance analysis, and fuzzy string matching (Jaro-Winkler) for robust skill and keyword mapping between resumes and job descriptions. Provides real-time ATS scoring, missing keyword identification, and actionable optimization suggestions.
Robust Authentication & Data Management
Implemented secure Google OAuth authentication using NextAuth.js v5 with PostgreSQL persistence via Prisma ORM. Features user session management, chat history tracking, and resume data versioning.
Serverless PDF Generation Pipeline
Built enterprise-grade PDF generation system using Puppeteer Core and @sparticuz/chromium optimized for Vercel serverless environments, creating high-quality, ATS-compatible resume exports with custom styling.
Advanced Template Engine
Developed a sophisticated template system with 10+ professionally designed layouts using Tailwind CSS, featuring responsive design, customizable styling, and ATS-friendly formatting for maximum compatibility.
Key Features
- AI-powered resume content suggestions using Gemini 2.0 Flash
- Advanced ATS compatibility analysis with RAG and NLP
- Real-time ATS scoring and optimization suggestions
- Semantic skill extraction and bidirectional matching
- Smart keyword extraction using TF-IDF analysis
- Fuzzy skill matching with Jaro-Winkler algorithm (85% threshold)
- 10+ professional ATS-optimized resume templates
- Live editing and real-time preview functionality
- High-quality PDF export with @sparticuz/chromium
- Google OAuth authentication with NextAuth.js
- Chat session management and persistence
- Responsive dark/light theme UI with Framer Motion animations
- Resume content validation and smart formatting
Screenshots
AI-powered resume building with natural language input
Add your own Gemini API key to use your quota instead of the shared one.
Chat naturally and see live preview with download option
Choose from Modern, Minimal, Classic and 10+ more templates
ATS analyser specific page of user custom resumes
ATS analysing of generated resume using ResumeGPT
Challenges & Solutions
Challenge: Gemini AI responses needed consistent structured output for resume data
Solution: Engineered comprehensive prompt engineering with strict JSON schema validation, custom parsing logic, and error handling to ensure reliable resume content generation with proper data types and formatting
Challenge: PDF generation in serverless environments faced memory and timeout constraints
Solution: Migrated from standard Puppeteer to @sparticuz/chromium with optimized configurations, implemented efficient rendering pipeline, and added proper error handling for reliable PDF generation in Vercel environment
Challenge: ATS compatibility while maintaining visual appeal across multiple templates
Solution: Researched ATS parsing requirements and implemented template designs that balance visual aesthetics with machine readability, using proper semantic HTML structure and optimized formatting
Challenge: Ensuring accurate ATS scoring and skill matching for diverse job descriptions and resume formats
Solution: Developed a robust NLP pipeline with RAG, TF-IDF, and fuzzy matching (Jaro-Winkler) to handle variations in job descriptions and resume content, ensuring reliable semantic skill extraction, bidirectional mapping, and real-time compatibility analysis across multiple industries.