1. 🚀 Product Summary Item Specification Product Name ResumeTailor AI One-line Description AI-powered tool that adapts a candidate's resume to a specific job description. Target Users Job seekers, freshers, career switchers Problem Creating a customized resume for every job is time-consuming and difficult. Value Proposition Convert an existing resume into a job-specific version with relevant skills, keywords, and experience emphasis. Primary Use Case Candidate wants to apply for a specific job and needs to quickly tailor their resume. Explicit facts: These come from the sample inputs. Assumptions: Resume is provided as PDF/DOCX or structured text, and the MVP focuses on tailoring rather than automatically applying to jobs. --- 2. 🎯 MVP Objective The MVP must prove one core hypothesis: > Job seekers will use an AI tool to tailor an existing resume to a specific job description because it saves them meaningful time and improves application relevance. The MVP does not need to become a complete career platform. The first version should successfully handle: Resume + Job Description → Analysis → Tailored Resume --- 3. 👥 Target User Primary User Job seeker applying to multiple jobs Has an existing resume. Finds different job descriptions online. Needs to customize their resume repeatedly. Wants faster preparation. May not understand which keywords or experiences should be emphasized. Secondary Users Fresh graduates Career switchers Freelancers seeking employment Professionals applying for different roles MVP Focus The initial MVP should focus on individual job seekers, not recruiters. Recruiter functionality should be postponed. --- 4. 🧭 Primary User Journey 1. User opens the website. 2. User creates an account or continues with supported authentication. 3. User uploads their resume. 4. User pastes a job description. 5. User clicks Analyze & Tailor. 6. System extracts relevant job requirements. 7. AI compares the resume with the job description. 8. System identifies: Matching skills Missing keywords Relevant experience Potential improvements 9. AI generates a tailored resume. 10. User reviews the changes. 11. User downloads the final resume. 12. User can optionally start another tailoring session. Core Value Moment The most important moment is: User receives a useful job-specific resume within minutes. --- 5. 🧩 MVP Feature Matrix Feature User Value Priority Complexity Dependencies MVP Status User authentication Save user sessions/history High Low Auth provider Must Have Resume upload Provides source resume High Medium File storage/parser Must Have Job description input Defines target job High Low Frontend Must Have JD analysis Identifies requirements High Medium LLM API Must Have Resume-JD comparison Shows relevance/gaps High Medium LLM + parser Must Have AI tailoring Creates customized resume Critical High LLM + structured data Must Have Resume preview Lets user review output High Medium Document renderer Must Have PDF download Allows application use Critical Medium PDF generation Must Have Saved history Reuse previous results Medium Medium Database Should Have Resume scoring Easy-to-understand feedback Medium Medium Scoring logic Should Have Multiple templates More customization Low Medium UI/document engine Could Have Cover-letter generation Additional application help Medium Medium LLM Future LinkedIn optimization Additional value Low Medium LLM Future Job-board integration Automated job discovery Low High External APIs Future Automatic job applications Automation Low Very High Job-board integrations Future MVP Principle The product does not need 20 features. The core MVP is: Upload → Analyze → Tailor → Review → Download --- 6. 🚫 Explicitly Out of Scope The following should deliberately NOT be built initially: Automatic job applications Job-board scraping LinkedIn automation Recruiter dashboard Multiple resume templates Mobile app Team accounts Advanced subscription management Interview preparation Career coaching AI interview simulator Automated networking Complex analytics dashboard Reason: None of these are necessary to validate whether users value AI-powered resume tailoring. --- 7. 📝 User Stories ID Epic User Story Priority Dependencies US-001 Authentication As a user, I want to create an account so that I can access my resume projects. Must Have Auth US-002 Resume As a user, I want to upload my resume so that the system can analyze it. Must Have Storage US-003 Job Input As a user, I want to paste a job description so that the system knows which role I am targeting. Must Have None US-004 Analysis As a user, I want the system to analyze the job description so that I understand its key requirements. Must Have LLM US-005 Matching As a user, I want my resume compared with the job description so that I can identify relevant skills and gaps. Must Have US-002, US-003 US-006 Tailoring As a user, I want an AI-tailored resume so that I can apply to the job faster. Must Have US-005 US-007 Preview As a user, I want to review the generated resume before downloading it so that I can correct inaccurate information. Must Have US-006 US-008 Download As a user, I want to download my tailored resume as PDF so that I can submit it to employers. Must Have US-007 US-009 History As a user, I want to see previous tailoring projects so that I can access them later. Should Have Database US-010 Feedback As a user, I want to provide feedback on the generated resume so that the product can improve. Should Have US-006 --- 8. ✅ Acceptance Criteria US-002: Resume Upload Given the user is on the resume upload page When they upload a supported resume file Then the system should validate the file and begin processing. Additional criteria: Unsupported file types are rejected. Oversized files are rejected. Processing status is displayed. Extracted text must be available before analysis begins. If extraction fails, the user receives an actionable error. --- US-003: Job Description Given the user has uploaded a resume When they paste a job description Then the system should accept and validate the content. Validation: Empty input is rejected. Very short descriptions produce a validation message. Long descriptions are handled within configured limits. User can edit the description before analysis. --- US-005: Resume-JD Comparison Given a valid resume and job description When the user starts analysis Then the system should identify relevant requirements and compare them with the resume. Output should include: Matching skills Relevant experience Missing/weak areas Important job keywords Suggested areas for improvement The system must not invent qualifications or experience that are absent from the user's resume. --- US-006: AI Tailoring Given a successfully analyzed resume and job description When the user requests tailoring Then the system should generate a job-specific resume. Requirements: Existing factual information should be preserved. No fabricated employment history. No fabricated degrees. No fabricated certifications. Relevant existing experience may be emphasized. Output should remain professionally structured. Generation failure should provide a retry option. --- US-007: Preview Given a generated resume When the user opens the preview Then they should be able to inspect the content before downloading. Important states: Loading Generated Generation failure Empty/invalid output --- US-008: Download Given the user has approved the generated resume When they click Download PDF Then a readable PDF should be generated. The PDF should: Preserve formatting. Contain the generated resume content. Open successfully. Avoid clipped text. Have a sensible filename. --- 9. 🎨 UX/Screens Specification Screen 1: Landing Page Purpose: Explain the product. Show core value proposition. CTA: Tailor My Resume Avoid a large marketing website for MVP. --- Screen 2: Authentication Required: Sign up Login Logout Basic account recovery --- Screen 3: Resume Upload Components: Upload area Supported formats File size guidance Processing indicator --- Screen 4: Job Description Components: Job title Company name - optional Job description text box Analyze button --- Screen 5: Analysis Display: Key requirements Matching skills Gaps Suggested improvements Primary CTA: Generate Tailored Resume --- Screen 6: Resume Preview Display: Generated resume Edit option Regenerate option Download PDF --- Screen 7: History Only if included in MVP. Display: Previous jobs Date Resume version Open/download action --- 10. 💻 Recommended Technology Stack Layer Recommendation Why Alternative Tradeoff Frontend Next.js Fast MVP development + full-stack React ecosystem React + Vite More backend separation Backend FastAPI Simple Python API + strong AI ecosystem Next.js API routes Adds separate service Database PostgreSQL Reliable relational database Supabase DB Supabase provides more managed functionality Authentication Supabase Auth Fast implementation Clerk Additional vendor dependency File Storage Supabase Storage Simple integration S3 S3 offers more control AI OpenAI API Strong LLM capabilities Anthropic API Model/provider differences PDF HTML/CSS → PDF Easy to control resume formatting ReportLab Alternative requires more document-specific coding Hosting Vercel + managed backend Simple deployment AWS AWS increases operational complexity Analytics PostHog Product usage analytics GA4 Different analytics model Monitoring Sentry Error tracking OpenTelemetry stack More setup CI/CD GitHub Actions Standard automated deployment/testing Vercel CI Less custom control Recommendation For a solo developer, a managed-service-heavy architecture is preferable. Do not begin with Kubernetes, microservices, Redis clusters, Kafka, or a complex cloud infrastructure. --- 11. 🏗️ Technical Architecture User | v Next.js Web App | +---- Authentication | +---- Resume Upload | +---- Job Description | v FastAPI Backend | +---- Resume Parser | +---- Job Description Analyzer | +---- Resume/JD Matching | +---- LLM Orchestrator | +---- Resume Generator | v PostgreSQL | +---- Users +---- Resumes +---- Jobs +---- Analyses +---- Generated Resumes File Storage | +---- Original Resume +---- Generated Documents External Services | +---- LLM API +---- Authentication +---- Analytics +---- Error Monitoring Core Flow Resume Upload ↓ Text Extraction ↓ Structured Resume Data ↓ Job Description ↓ Requirement Extraction ↓ Resume ↔ JD Comparison ↓ Tailoring Prompt ↓ Structured AI Output ↓ Resume Renderer ↓ PDF --- 12. 🗄️ Data Model User Fields: id email created_at Relationship: User → many Resumes User → many Tailoring Projects --- Resume Fields: id user_id file_url extracted_text created_at updated_at Relationship: User 1 → Many Resumes --- Job Fields: id user_id title company description created_at --- Analysis Fields: id resume_id job_id matched_skills missing_skills requirements recommendations created_at --- TailoredResume Fields: id analysis_id generated_content pdf_url version created_at --- 13. 🔌 API Specification Method Endpoint/Operation Purpose Input Output Auth POST /resumes Upload resume File Resume ID Yes GET /resumes/:id Retrieve resume Resume ID Resume data Yes POST /jobs Create job Job data Job ID Yes POST /analysis Analyze resume against JD Resume ID + Job ID Analysis Yes POST /tailor Generate tailored resume Analysis ID Resume version Yes GET /tailored/:id Retrieve generated resume ID Resume data Yes GET /tailored/:id/pdf Download PDF ID PDF Yes GET /projects Retrieve history User Projects Yes Avoid creating separate APIs for every small UI operation. --- 14. 🔐 Security & Reliability Baseline Minimum requirements: Secure authentication. Authorization checks for every user-owned resource. Users cannot access another user's resume. HTTPS. Secure API keys/secrets. File type validation. File size limits. Input validation. Rate limiting on expensive AI operations. Server-side authorization. Secure storage for uploaded resumes. Error logging. Basic database backups. Dependency vulnerability scanning. High-Risk Area Resume privacy is especially important because resumes contain personal information such as: Names Email addresses Phone numbers Employment history Education Potentially addresses The MVP should minimize unnecessary data retention. --- 15. 🧪 Testing Strategy Functional Tests Test: Account creation Login Resume upload Invalid file upload Job description submission Resume analysis AI generation Resume preview PDF generation Download AI Quality Tests Create a fixed evaluation dataset containing different resumes and job descriptions. Check: No fabricated experience. No fabricated qualifications. Relevant skills are identified. Job requirements are extracted correctly. Existing experience is preserved. Output remains readable. Security Tests Verify: User A cannot access User B's resume. Invalid authentication tokens are rejected. Uploaded files are validated. API endpoints enforce authorization. Sensitive secrets are not exposed. --- 16. 📊 MVP Success Metrics Primary Metric Successful Tailoring Rate Percentage of users who: Upload Resume → Submit JD → Generate Resume → Download This measures whether the core workflow actually works. Supporting Metrics Resume uploads Completed analyses Tailored resumes generated PDF downloads Time to first generated resume Generation failure rate Repeat tailoring sessions User feedback score Sample Validation Target From the provided input: 100 beta users and: 30% repeat usage These are treated as user-provided success targets, not market evidence. --- 17. 🚀 Development Roadmap Stage 1 — Foundation Build: 1. Next.js application 2. Authentication 3. PostgreSQL 4. File storage 5. Basic UI 6. API structure Critical dependency: Authentication + database + storage. --- Stage 2 — Core User Flow Build: 1. Resume upload 2. Resume text extraction 3. Job description input 4. AI requirement extraction 5. Resume/JD comparison Critical path: Upload Resume ↓ Extract Text ↓ Add JD ↓ Analyze --- Stage 3 — Essential Features Build: 1. Tailored resume generation 2. Resume preview 3. Editing 4. PDF generation 5. Download At the end of this stage, the core MVP should be usable. --- Stage 4 — Quality & Security Add: Authorization testing Input validation AI hallucination safeguards Rate limits Error monitoring Analytics PDF quality testing Backup configuration --- Stage 5 — MVP Launch Launch to a small beta group. Collect: Successful generation rate User feedback Editing frequency Download rate Repeat usage AI errors Do not immediately build the future feature list. --- 18. ⚠️ Product & Technical Risks Risk Likelihood Impact Mitigation Validation AI invents candidate information Medium High Structured generation + validation Test generated resumes Poor resume parsing Medium High Support limited file formats initially Test real resumes Users don't trust AI output Medium High Editable preview + transparency User interviews PDF formatting problems Medium Medium Fixed MVP template PDF testing LLM costs become high Medium Medium Token limits + usage controls Track cost/session Users want more than tailoring Medium Medium Keep MVP focused User feedback Privacy concerns Medium High Secure storage + retention policy Security review --- 19. 📌 Assumptions & Open Questions Assumptions Users already have a resume. Users manually provide job descriptions. MVP targets web users. AI-generated content requires human review. PDF is sufficient for the initial output. Users are responsible for verifying the final resume. Open Questions 1. Which resume formats should MVP support? 2. Should users be able to edit generated resumes directly? 3. Should the system preserve the user's original formatting or use a standard template? 4. Which AI model provides the best quality/cost balance? 5. How long should uploaded resumes be retained? 6. Should authentication be mandatory before the first generation? 7. Should free users have a generation limit? These should be validated before expanding the product. --- 20. 🏁 FINAL MVP BUILD SPECIFICATION Product Scope Build a web application that allows a job seeker to: Upload Resume → Add Job Description → Analyze Match → Generate Tailored Resume → Review → Download PDF Must-Have Features User authentication Resume upload Resume text extraction Job description input Job requirement analysis Resume/JD comparison AI resume tailoring Resume preview PDF generation PDF download Basic privacy/security controls Critical User Stories US-002 Resume Upload US-003 Job Description US-005 Resume-JD Comparison US-006 AI Tailoring US-007 Resume Preview US-008 PDF Download Critical Acceptance Criteria Resume can be successfully processed. JD can be analyzed. Resume and JD can be compared. AI does not fabricate candidate information. Generated resume can be reviewed. PDF is readable and downloadable. User data is isolated between accounts. Recommended Stack Next.js + FastAPI + PostgreSQL + Supabase Storage/Auth + OpenAI API + Vercel + PostHog + Sentry Core Architecture Start with a modular monolithic architecture, not microservices. Data Requirements Users Resumes Jobs Analyses Tailored resumes Essential APIs Resume upload/retrieval Job creation Analysis Tailoring Generated resume retrieval PDF download Project history Security Baseline Authentication Authorization HTTPS Secure secrets File validation Rate limiting Private storage Error monitoring Backup Testing Requirements Test the complete journey: Sign up → Upload → JD → Analyze → Tailor → Review → Download Also specifically test AI hallucination/fabrication and cross-user data access. Launch Criteria The MVP is ready when: Core user journey works end-to-end. No critical security issues remain. Generated resumes are consistently usable in beta testing. PDF output is reliable. Analytics are working. AI generation failures are handled gracefully. Users can complete the primary task without developer assistance. Immediate Next Steps 1. Validate the problem with 5-10 target users. 2. Define the exact resume formats for MVP. 3. Create the wireframes for the 5-6 core screens. 4. Set up Next.js + FastAPI + PostgreSQL. 5. Implement authentication and file storage. 6. Build resume parsing. 7. Implement JD analysis. 8. Implement resume/JD comparison. 9. Build structured AI tailoring. 10. Add preview and PDF generation. 11. Add security and analytics. 12. Run a small beta before adding any post-MVP features. Core MVP principle: Don't build an "AI career platform" yet. Build one thing exceptionally well: turn an existing resume into a credible, job-specific resume quickly and safely. 😊