# QUICK START: AI-ACCELERATED PROJECT TEMPLATE

## Copy this template for every new project

---

## PROJECT BRIEF

**Project Name**: _______________________

**Business Goal**: _______________________

**Target Completion**: _______ hours (human time) / _______ equivalent hours

**Release Date**: _______________________

---

## STEP 1: PRE-FLIGHT CHECKLIST (5 minutes)

```bash
# Verify AI tools are configured
[ ] GitHub Copilot active in IDE
[ ] Claude Code CLI available
[ ] CI/CD pipelines configured
[ ] Monitoring tools set up
[ ] Security scanners enabled
```

---

## STEP 2: PROJECT INITIALIZATION (30 minutes)

### AI Prompt for Requirements Generation:

```
You are an AI project accelerator. I need a production-ready [PROJECT_TYPE] that [BUSINESS_OBJECTIVE].

Requirements:
- Timeline: [X hours]
- Users: [Expected number]
- Key Features: [List 3-5 key features]
- Technology Stack: [Your preferences or "recommend optimal stack"]

Generate:
1. Detailed technical specifications
2. Complete user stories with acceptance criteria
3. Database schema design
4. API contract (OpenAPI spec)
5. Architecture diagram (Mermaid format)
6. Comprehensive test scenarios
7. Security requirements checklist
8. Performance benchmarks

Output everything in structured markdown format.
Execute without asking for confirmation.
Make production-ready decisions.
```

---

## STEP 3: ARCHITECTURE GENERATION (1 hour)

### AI Prompt for Architecture:

```
Based on the requirements above, generate complete project architecture:

1. System architecture (microservices/monolith/serverless)
2. Database design (normalized schema with indexes)
3. API design (REST/GraphQL with full specifications)
4. Authentication/authorization strategy
5. Caching strategy
6. Deployment architecture (cloud provider, containers, orchestration)
7. Monitoring and observability plan
8. Disaster recovery and backup strategy

Provide:
- Mermaid diagrams for all architectures
- Complete database migrations
- OpenAPI/Swagger specs
- Infrastructure as Code (Terraform/Pulumi)
- Docker and docker-compose files
- CI/CD pipeline configuration

Execute fully. No confirmations needed.
```

---

## STEP 4: DEVELOPMENT KICKOFF (2-3 hours)

### AI Prompt for Project Scaffolding:

```
Generate complete project scaffold:

Technology Stack:
- Backend: [FRAMEWORK]
- Frontend: [FRAMEWORK]
- Database: [DATABASE]
- Cloud: [PROVIDER]

Generate:
1. Complete project structure with all folders
2. Configuration files (env, docker, package.json, etc.)
3. Base models/entities with validation
4. Database migrations and seeds
5. API routes with controllers
6. Authentication middleware
7. Error handling middleware
8. Logging configuration
9. Testing setup (unit, integration, e2e)
10. CI/CD pipeline
11. Documentation structure

Use best practices and production-ready patterns.
Generate 100% working code.
No placeholders or TODOs.
```

---

## STEP 5: PARALLEL DEVELOPMENT STREAMS

### Stream 1: Backend Development

```
AI Command: Generate complete backend implementation

Features to implement:
[List your features here]

For each feature generate:
- Database models with relationships
- Business logic services
- API endpoints (with full CRUD)
- Input validation
- Error handling
- Unit tests (100% coverage)
- Integration tests
- API documentation

Requirements:
- Production-ready code
- Security best practices
- Performance optimized
- Fully tested
- Documented

Execute all features in parallel where possible.
```

### Stream 2: Frontend Development

```
AI Command: Generate complete frontend implementation

Pages/Features:
[List your UI features here]

For each generate:
- Responsive React/Vue/Angular components
- State management (Redux/Vuex/NgRx)
- API integration with error handling
- Form validation
- Loading and error states
- Routing
- Authentication UI
- Unit tests for components
- E2E tests for user flows
- Storybook stories

Requirements:
- Mobile-first responsive
- Accessibility compliant (WCAG 2.1)
- Performance optimized (Lighthouse > 90)
- Cross-browser compatible

Execute all in parallel.
```

### Stream 3: Testing & Quality

```
AI Command: Generate comprehensive test suite

Generate:
1. Unit tests for all backend services (100% coverage)
2. Unit tests for all frontend components (90%+ coverage)
3. Integration tests for all API endpoints
4. E2E tests for critical user journeys
5. Performance tests (load, stress, spike)
6. Security tests (OWASP Top 10)
7. Visual regression tests
8. Accessibility tests

Configure:
- CI/CD to run all tests automatically
- Quality gates (must pass before merge)
- Code coverage reporting
- Performance budgets

Execute fully automated test generation.
```

### Stream 4: DevOps & Infrastructure

```
AI Command: Generate complete DevOps setup

Generate:
1. Infrastructure as Code (Terraform/Pulumi)
   - Production environment
   - Staging environment
   - Development environment
2. CI/CD Pipelines
   - Build, test, deploy automation
   - Blue-green or canary deployments
   - Automated rollback
3. Monitoring & Observability
   - Application monitoring
   - Error tracking
   - Performance monitoring
   - Log aggregation
4. Security
   - Automated vulnerability scanning
   - Secret management
   - SSL/TLS configuration
5. Backup & Disaster Recovery
   - Automated backups
   - Recovery procedures

Execute complete infrastructure setup.
```

---

## STEP 6: VALIDATION CHECKPOINTS

### Automated Quality Gates

Run these commands (AI-generated scripts):

```bash
# 1. Run all tests
npm test                    # or: pytest, dotnet test, etc.
npm run test:e2e
npm run test:integration

# 2. Code quality checks
npm run lint
npm run format:check
npm run type-check

# 3. Security scans
npm audit
snyk test
npm run security:scan

# 4. Performance tests
npm run test:performance
npm run lighthouse

# 5. Build verification
npm run build
docker-compose up -d
npm run health-check

# All must pass 100% before proceeding
```

---

## STEP 7: DEPLOYMENT

### AI Prompt for Deployment:

```
Deploy the application:

1. Verify all quality gates passed
2. Deploy to staging environment
3. Run smoke tests on staging
4. Generate deployment documentation
5. Create rollback plan
6. Deploy to production
7. Verify production health
8. Configure monitoring and alerts

Execute automated deployment.
Report only if errors occur.
```

---

## STEP 8: DOCUMENTATION

### AI Prompt for Documentation:

```
Generate complete documentation:

1. API Documentation
   - Auto-generated from OpenAPI spec
   - Request/response examples
   - Authentication guide
   - Error codes reference

2. User Guides
   - Getting started
   - Feature walkthroughs
   - Troubleshooting

3. Developer Documentation
   - Architecture overview
   - Setup instructions
   - Contributing guidelines
   - Code style guide

4. Operations Documentation
   - Deployment guide
   - Monitoring guide
   - Backup and recovery
   - Incident response

Host on: [GitBook/Mintlify/Docusaurus]

Auto-update on every code change.
```

---

## TIME TRACKING

| Phase | Estimated Time | Actual Time | Productivity Multiplier |
|-------|---------------|-------------|------------------------|
| Requirements | 1-2 hours | _____ | _____ |
| Architecture | 2-4 hours | _____ | _____ |
| Development | 10-20 hours | _____ | _____ |
| Testing | 2-4 hours | _____ | _____ |
| Deployment | 1-2 hours | _____ | _____ |
| Documentation | 1-2 hours | _____ | _____ |
| **TOTAL** | **17-34 hours** | **_____** | **_____** |

---

## SUCCESS CRITERIA (Must Achieve 100%)

- [ ] All features implemented and working
- [ ] Test coverage > 90%
- [ ] All security scans passed (0 critical vulnerabilities)
- [ ] Performance benchmarks met
- [ ] Documentation complete
- [ ] Successfully deployed to production
- [ ] Monitoring active and reporting
- [ ] Zero critical bugs in production (first 24 hours)

---

## EMERGENCY CONTACTS

- AI Tool Support: [Links]
- Cloud Provider Support: [Links]
- Team Lead: [Contact]
- Emergency Rollback Command: `./rollback.sh`

---

## POST-LAUNCH CHECKLIST

- [ ] Monitor error rates (first 24 hours)
- [ ] Verify all monitoring alerts working
- [ ] Check performance metrics
- [ ] Review user feedback
- [ ] Document lessons learned
- [ ] Update AI prompts based on learnings
- [ ] Schedule post-mortem review

---

## AI TOOL OPTIMIZATION NOTES

What worked well:
- _______________________
- _______________________

What could be improved:
- _______________________
- _______________________

New AI tools to try:
- _______________________
- _______________________

---

**PROJECT STATUS**: [ ] Planning [ ] In Progress [ ] Testing [ ] Deployed [ ] Complete

**Last Updated**: _______________________
