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Part III Technical Design
Alp Yalay edited this page Aug 10, 2025
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Part III creates a Technical Design Document that defines HOW to build the product outlined in your PRD using the best tools available in 2025. It makes architecture decisions, selects technology stacks, and plans implementation approaches.
- PRD Document (from Part II) - Required for alignment
- Research Findings (from Part I) - Optional but helpful for context
-
.txt,.pdf,.docx,.mdfiles - Direct text paste for short content
Continues the three-level system:
- A) Vibe-coder - Limited coding, using AI to build everything
- B) Developer - Experienced programmer
- C) Somewhere in between - Some basics, still learning
- Platform preference (web, mobile app, desktop, or help decide)
- Coding approach (no-code, AI writes all code, learning basics)
- Budget constraints (free only, up to $50/month, $200/month, flexible)
- Launch timeline (1-2 weeks, 1 month, 2-3 months, no rush)
- Main concerns (getting stuck, costs, security, wrong choices)
- Previous tool experience (any AI tools or platforms tried)
- Feature priorities (simple to build, perfect functionality, visual appeal, scalability)
- Platform strategy with reasoning
- Preferred tech stack (frontend, backend, database, infrastructure, AI integration)
- Architecture pattern (monolithic, microservices, serverless, JAMstack)
- Service integration (authentication, storage, payments, email, analytics)
- AI coding assistance strategy (tools and workflow)
- Development workflow (Git strategy, CI/CD, testing, environments)
- Performance and scaling (expected load, data volume, distribution)
- Security and compliance (data sensitivity, regulations, authentication)
- Platform decision with guidance on trade-offs
- Technical comfort zone (known languages, frameworks, learning preferences)
- Development approach (no-code, low-code with AI, learn by doing)
- Technical complexity assessment of required features
- Budget reality check for tools and services
- AI assistance preferences (level of automation desired)
- Timeline and capacity (available hours, launch deadline, beta users)
- Claude 4.1 Opus - Best for architecture decisions
- Gemini 2.5 Pro - Best for complex trade-off analysis
- GPT-5 - Good for quick technical iterations
Output complexity varies by user level:
- Recommended approach with specific tool selection
- Alternative options comparison table
- Step-by-step setup instructions
- Feature implementation guides for each PRD feature
- AI assistance strategy with prompt templates
- Deployment plan with one-click options
- Cost breakdown development and production phases
- Learning resources curated for their stack
- Limitations awareness with workarounds
- Architecture overview with diagrams
- Tech stack decisions with detailed rationale
- Component design frontend and backend structures
- Database schema with relationships
- Feature implementation patterns and APIs
- Security implementation authentication and authorization
- Performance optimization caching and scaling strategies
- Development workflow AI-assisted development
- Testing strategy unit, integration, and E2E approaches
- Deployment infrastructure as code
- Monitoring observability and metrics
- Cost analysis development and running costs
- Risk mitigation technical and business risks
- Balanced approach recommendation
- Project structure with explanations
- Implementation phases with learning objectives
- AI prompting guide effective strategies
- Simplified architecture conceptual understanding
- Step-by-step implementation with checkpoints
- Common challenges solutions and debugging approaches
- Learning resources progressive skill development
- Growing beyond MVP scaling path
- Platform selection (web, mobile, desktop)
- Architecture patterns (monolithic, microservices, serverless)
- Technology stack recommendations
- Database and storage solutions
- API design approaches
- Development environment setup
- Project structure organization
- Feature implementation strategies
- Testing approaches
- Error handling patterns
- Hosting platform recommendations
- Environment configuration
- Monitoring and logging
- Scaling considerations
- Cost optimization
- AI tool selection for development
- Prompt engineering approaches
- Code generation patterns
- Debugging methodologies
- Learning resources
- React/Next.js for web applications
- React Native/Flutter for mobile
- Vanilla JavaScript for simple projects
- No-code platforms for rapid development
- Node.js/Express for JavaScript developers
- Python/FastAPI for data-heavy applications
- Serverless functions for simple APIs
- Backend-as-a-Service (Supabase, Firebase)
- PostgreSQL for relational data
- MongoDB for document storage
- Supabase for integrated backend
- SQLite for simple applications
Technical Design documents include:
- Development costs (tools, services, time)
- Running costs (hosting, databases, third-party services)
- Scaling costs (projected growth scenarios)
- Free tier utilization (maximize cost-effectiveness)
- Question answering: 10-15 minutes
- Technical design generation: 5-10 minutes
- Total time: 15-25 minutes
Generated Technical Design saved as:
TechDesign-[AppName]-MVP.md
Before proceeding to Part IV:
- Tech stack aligns with skill level and timeline
- Architecture supports PRD requirements
- Cost estimates fit budget constraints
- Deployment approach is clearly defined
- AI assistance strategy matches user needs
After completing Part III:
- Save Technical Design as
TechDesign-[AppName]-MVP.md - Review alignment with PRD requirements
- Proceed to Part IV: AI Agent Instructions
- Use Technical Design to guide implementation decisions