Context for new readers:
This document builds on my earlier blog about vibe coding an RFI where I explored whether a single “vibe coder” could respond to a government Request for Information (RFI) and even begin building the product.
Here, I’ve reframed that exercise as a formal project brief, as if written by a product vendor. It also includes an architectural view to highlight what an internal team might consider.
1. Executive Brief (Senior Executive)
Context: TEC seeks to add a CV and Cover Letter building capability to its Tahatū Career Navigator site.
Purpose: Support learners to create tailored, professional documents integrated into the wider careers ecosystem.
Core Requirements (based on RFI themes)
- Learner-focused: Easy creation, editing, and storage of CVs and CLs.
- Tailoring: Ability to customise for specific jobs, industries, or employers.
- Inclusion: Meet WCAG 2.2; bilingual (English/te reo Māori); culturally aware.
- Integration: Pulls from Tahatū learner profiles, career planning, and micro-credentials.
- AI Use: Assist, not replace; focus on guidance, tone, and keyword match.
- Data Sovereignty: Hosted in NZ; compliant with the Privacy Act 2020.
- Sustainability: Affordable, scalable, with a roadmap for improvement.
2. Product Requirements (Product Owner)
Feature Highlights
- Narrative/Upload input with LLM parsing.
- Master CV creation with live template previews.
- Tailored CV & CL generation from job ads.
- Editable cover letters with tone options.
- Template library (system + user uploaded).
- Application tracking with outcomes and feedback.
- Analytics dashboard (usage, success rates).
- Accessibility baked into every flow.
- Privacy: clear consent, no retention without approval.
3. Roadmap (Architectural View)
Phase 1 – MVP
- Basic auth, profile parsing, 1–2 templates, cover letter generator, PDF export.
Phase 2 – Growth
- Multiple templates, ATS optimisation, job ad tailoring, DOCX export, feedback loop.
Phase 3 – Advanced
- Analytics, employer integration, multilingual support, PWA/offline.
4. Architectural View
- Frontend: Next.js, React, TypeScript, Tailwind, shadcn/ui.
- Backend: Vercel serverless API routes.
- Database: Supabase Postgres.
- Storage: Supabase Storage for files/templates.
- Auth: NextAuth, SSO options.
- AI: OpenAI/Azure orchestration for parsing and generation.
- Exports: Playwright PDF; docx-templater.
- Validation: Zod schema enforcement.
- Observability: Logging and token use tracking.
Why this stack? Fast to deploy, flexible, aligns with SaaS patterns, ensures sovereignty.
5. TEC Architectural View
MoSCoW Priorities
| Priority | Features |
|---|---|
| Must-Have | Accessibility, CV & CL creation, integration with Tahatū, NZ hosting |
| Should-Have | ATS scoring, analytics, multi-template support |
| Could-Have | Employer integrations, offline mode, advanced AI tone settings |
| Won’t-Have | Recruiter portals, job scraping (initially) |
Integration Factors
- Single sign-on with Tahatū accounts.
- APIs to share CVs securely with job boards.
- Consider mobile-first performance and offline use.
AI Considerations
- Encourage uniqueness: multiple template styles, adjustable tone, and user review steps.
- Avoid “sameness” by offering variant phrasing and style libraries.
- Ensure ethical guardrails: clear consent, privacy-first.
Additional Context
- Incorporate cultural elements and co-design with Māori and Pacific stakeholders.
- Monitor outcomes: track whether users get interviews/jobs.
- Plan for maintainability: budget and roadmap must support continuous improvement.
Written for KiwiGPT.co.nz — Generated, Published and Tinkered with AI by a Kiwi