Intelligent Application Tracking with AI Copilot Assistant
This expert-level architectural design prompt provides comprehensive guidance for building a production-ready AI-powered job search and application tracking platform. It focuses on intelligent job matching, resume optimization, ATS compatibility scoring, and an embedded AI copilot that guides users throughout their job search journey.
The prompt covers complete system design including React 18 with TypeScript frontend, Spring Boot backend, MySQL database, Redis caching, OpenAI GPT-4 integration for resume parsing and AI assistance, multi-factor job matching engine, ATS resume scoring, comprehensive application tracking, and production-grade security, observability, and deployment architecture.
Context-aware AI assistant powered by GPT-4 that helps refine job searches, explains job descriptions, suggests resume improvements, generates interview questions, and supports multi-turn conversations
Multi-factor matching engine calculating 0-100 scores based on skills match (40%), experience (25%), location (20%), salary alignment (10%), and job type preference (5%) with transparent explanations
Comprehensive ATS scoring engine with subscores for readability, keyword match, format compliance, length optimization, and content quality. Provides actionable suggestions for improvement
End-to-end application tracking with status lifecycle management, timeline of events, notes and reminders, interview scheduling, dashboard metrics, and color-coded status visualization
## Prompt: Design an AI-Powered Job Search & Application Tracking Platform
You are a **senior full-stack architect, product designer, and AI systems expert**.
Design a **production-ready, enterprise-grade AI-powered job search platform** that helps job seekers discover relevant jobs, optimize resumes, track applications, and prepare for interviews.
### 1. Platform Overview
Design a **full-stack web application** that:
* Helps users search and discover jobs intelligently
* Manages multiple resumes and cover letters
* Scores resumes for ATS compatibility
* Tracks job applications end-to-end
* Uses an embedded AI assistant ("AI Copilot") to guide users throughout their job search journey
The platform must feel **modern, intelligent, and personalized**, suitable for large-scale usage.
---
### 2. Architecture & Tech Stack
Define a scalable architecture using:
* **Frontend**: React 18 + TypeScript, Tailwind CSS, Shadcn UI
* **Backend**: Spring Boot (Java 17+)
* **Database**: MySQL
* **Caching**: Redis (with TTL & invalidation strategy)
* **File Storage**: S3-compatible object storage
* **AI**: OpenAI GPT-4 for resume parsing, ATS scoring, and AI Copilot
* **Authentication**: JWT + Google OAuth
* **Observability**: Structured logging, correlation IDs, performance logs
Explain:
* Service boundaries
* Data flow
* Caching strategy
* AI integration points
---
### 3. Authentication & Authorization
Design:
* Email/password registration with strong password rules
* Email verification
* Google OAuth login
* JWT-based authentication with refresh tokens
* Role-based access control (USER, ADMIN)
* Secure password reset using expiring tokens
* BCrypt password hashing
---
### 4. Job Search & Discovery
Design advanced job discovery with:
* Full-text search and filters:
* Location, job type, experience level
* Remote/Hybrid/Onsite
* Salary range
* Skills, company size, industry
* Job data imported via CSV (LinkedIn-style format)
* Job cards showing:
* Company logo
* Title, location, salary
* Posting date
* **Personalized match score**
* Detailed job pages with:
* Description, requirements
* Company info
* Similar job recommendations
* Visual breakdown of match score
---
### 5. AI-Powered Job Matching
Design a **multi-factor matching engine** that calculates a **0–100 score** using:
* Skills match (40%)
* Experience match (25%)
* Location preference (20%)
* Salary alignment (10%)
* Job type preference (5%)
Include:
* Transparent "why this job matches you" explanations
* Cached match scores
* Human-readable insights to help users improve profiles
---
### 6. AI Copilot Assistant
Design an embedded **context-aware AI assistant** that:
* Understands where the user is (jobs, resume, application, interview)
* Helps refine job searches
* Explains job descriptions
* Suggests resume improvements
* Generates interview questions
* Supports multi-turn conversations
* Stores conversation history
* Implements rate limiting, cost controls, and error handling
---
### 7. Resume Management
Design a resume system that:
* Supports uploading PDF/DOCX (≤5MB)
* Allows multiple resumes per user (with one primary resume)
* Uses:
* Apache Tika / PDF parsing
* GPT-4 for structured entity extraction
* Extracts:
* Personal info
* Experience
* Education
* Skills (categorized)
* Projects
* Certifications
* Languages
* Provides:
* Inline editing
* Resume duplication
* Fallback regex parsing if AI fails
---
### 8. ATS Resume Scoring
Design an ATS scoring engine with:
* Overall score (0–100)
* Subscores:
* Readability
* Keyword match
* Format compliance
* Length optimization
* Content quality
* Detailed, actionable suggestions:
* Missing keywords
* Formatting issues
* Weak action verbs
* Lack of quantifiable results
* Strengths vs weaknesses analysis
---
### 9. Application Tracking System (ATS)
Design an application tracker that:
* Tracks status lifecycle:
* Applied → Viewed → Interview → Offer → Outcome
* Supports:
* Resume & cover letter selection
* Timeline of events
* Notes and reminders
* Provides:
* Dashboard metrics
* Filters by status, company, date
* Color-coded statuses
* Detailed application history view
---
### 10. Notes, Reminders & Interviews
Include:
* Structured notes (general, follow-up, interview prep)
* Pinned notes
* Reminder notifications
* Interview scheduling with:
* Type, date, duration
* Virtual/onsite details
* Calendar/email reminders
* Post-interview feedback
---
### 11. Cover Letter Management
Design cover letter features:
* Manual creation
* Upload existing letters
* AI-generated letters using GPT-4
* Tone selection
* Templates by role/industry
* Reuse & versioning
* Association with jobs and resumes
---
### 12. Document Management
Support:
* Uploading portfolios, references, certificates
* Secure storage
* Application-level document association
* Timeline tracking of uploads
---
### 13. User Profile & Preferences
Allow users to manage:
* Personal info
* Skills & experience
* Job preferences (location, salary, job type)
* Notification preferences
* Profile data feeding job matching logic
---
### 14. Admin Capabilities
Design admin features for:
* User management
* Role management
* Job CRUD operations
* Bulk CSV job imports
* Import validation & error reporting
* Platform analytics dashboards
* Full audit logs for admin actions
---
### 15. Caching, Logging & Monitoring
Define:
* Redis caching TTLs & eviction
* Cache invalidation rules
* Structured logging with trace IDs
* Security logs
* Performance logs
* Error logs with retention policy
---
### 16. Error Handling & Validation
Design:
* Global exception handling
* Consistent error response format
* Custom exception types
* Field-level validation messages
* Backend + frontend validation alignment
---
### 17. Security Model
Cover:
* JWT security
* Role-based authorization
* Resource ownership checks
* Rate limiting
* CORS policies
* Secure file uploads
* Presigned URLs
* Secrets via environment variables
---
### 18. Performance & Scalability
Explain:
* Indexing strategy
* Pagination
* Query optimization
* Connection pooling
* React performance optimizations
* API request deduplication
* Search debouncing
---
### 19. Testing Strategy
Design:
* Backend unit & integration tests
* Frontend component tests
* API contract tests
* Optional E2E tests
* CI-enforced coverage & quality gates
---
### 20. Deployment Architecture
Design containerized deployment using:
* Docker
* Kubernetes
* Nginx for frontend
* Health checks
* Rolling deployments
* Secrets & config management
* Observability stack
---
### 21. Future Enhancements
Propose:
* AI interview simulation
* Skill gap analysis
* Career path recommendations
* Job alerts
* Analytics insights
* Mobile apps
* Community & collaboration features
---
### Output Expectations
Produce:
* Clear architecture explanations
* Entity relationships
* Feature-by-feature breakdown
* Trade-offs and best practices
* Production-ready design decisions
Do **not** generate actual code unless explicitly asked.AI Copilot Design: Focus on context-awareness - the AI assistant should understand where the user is in their job search journey and provide relevant, actionable guidance. Implement rate limiting and cost controls for GPT-4 API calls.
Multi-Factor Matching: Design the job matching engine with weighted factors and transparent scoring. Provide clear explanations for why jobs match or don't match user profiles to help them improve their search criteria and profiles.
ATS Optimization: Implement comprehensive ATS scoring with subscores for different aspects. Provide specific, actionable suggestions for improving resume ATS compatibility rather than generic advice.
Application Tracking: Design a complete lifecycle tracking system with status management, timeline visualization, notes, reminders, and interview scheduling. Integrate calendar reminders and email notifications for key events.
Resume Intelligence: Use Apache Tika or PDF parsing libraries combined with GPT-4 for accurate resume parsing. Always implement fallback regex parsing if AI extraction fails to ensure reliability.