Apply your knowledge to build something amazing!
By the end of this project, you will:
This capstone project challenges you to build a production-grade application leveraging AI-assisted development methodologies. You'll demonstrate mastery of prompt engineering, code generation, debugging, testing, and deployment while creating a solution that showcases your ability to integrate AI into real-world applications.
Choose ONE project that aligns with industry demands:
Industry Focus: Productivity & Project Management
Build an intelligent task management system with:
Technical Stack Suggestion: React/Vue.js + Node.js/Python + OpenAI API/Claude API
Industry Focus: EdTech & Corporate Training
Create an adaptive learning platform featuring:
Technical Stack Suggestion: Next.js + FastAPI + LangChain + Vector Database
Industry Focus: Developer Tools & DevOps
Develop a comprehensive code quality tool with:
Technical Stack Suggestion: TypeScript + AST Parsers + LLM APIs + GitHub Integration
Industry Focus: FinTech & Personal Finance
Build a sophisticated financial management system with:
Technical Stack Suggestion: Python/Django + PostgreSQL + OpenAI API + Data Visualization Libraries
graph TB
subgraph "Frontend Layer"
UI[User Interface]
CM[Component Manager]
SM[State Management]
end
subgraph "API Gateway"
RT[Route Handler]
AU[Authentication]
RL[Rate Limiter]
end
subgraph "Business Logic"
SV[Service Layer]
AI[AI Integration Layer]
VL[Validation Layer]
end
subgraph "Data Layer"
DB[(Database)]
CH[Cache]
FS[File Storage]
end
subgraph "AI Services"
LLM[LLM API]
ML[ML Models]
NLP[NLP Service]
end
UI --> CM --> SM --> RT
RT --> AU --> RL --> SV
SV --> AI --> LLM
SV --> VL --> DB
AI --> ML
AI --> NLP
SV --> CH
SV --> FS
Deliverables:
Success Criteria:
Deliverables:
Success Criteria:
Deliverables:
Success Criteria:
Deliverables:
Success Criteria:
Deliverables:
Success Criteria:
Minimum Requirements:
Documentation Requirements:
# AI Code Generation Log
## Component: [Component Name]
**Initial Prompt:**
[Exact prompt with context]
**AI Response Analysis:**
- Lines of code generated: X
- Modifications required: Y
- Performance impact: Z
**Optimization Iterations:**
1. [First refinement prompt and result]
2. [Second refinement prompt and result]
**Final Implementation:**
- File: [path/to/file]
- Test coverage: X%
- Performance metrics: [metrics]
Required Implementations:
Debug Documentation Template:
## Debug Session: [Issue ID]
### Problem Context
- Error type: [Classification]
- Affected components: [List]
- User impact: [Description]
### AI Analysis Process
1. **Initial prompt:** [Prompt]
2. **AI hypothesis:** [Summary]
3. **Validation steps:** [List]
4. **Solution implementation:** [Code]
5. **Verification results:** [Metrics]
### Lessons Learned
- Pattern identified: [Description]
- Prevention strategy: [Approach]
Required Deliverables:
// Example: AI-Assisted Code Review Implementation
class AICodeReviewer {
async reviewCode(codeSnippet, context) {
const prompt = `
Review this ${context.language} code for:
1. Security vulnerabilities
2. Performance issues
3. Best practice violations
4. Potential bugs
Code:
${codeSnippet}
Provide specific line numbers and actionable fixes.
`;
const review = await this.aiService.analyze(prompt);
return this.formatReview(review);
}
formatReview(aiResponse) {
// Parse and structure AI response
return {
critical: aiResponse.security,
warnings: aiResponse.performance,
suggestions: aiResponse.bestPractices,
metrics: this.calculateCodeQuality(aiResponse)
};
}
}
# Example: AI-Enhanced Error Recovery System
class AIErrorHandler:
def __init__(self, ai_service):
self.ai_service = ai_service
self.error_patterns = {}
async def handle_error(self, error, context):
# Analyze error with AI
analysis = await self.ai_service.analyze_error({
'error': str(error),
'stack_trace': error.__traceback__,
'context': context,
'historical_fixes': self.error_patterns.get(type(error))
})
# Generate recovery strategy
recovery_plan = await self.generate_recovery_plan(analysis)
# Execute recovery with monitoring
return await self.execute_recovery(recovery_plan)
async def generate_recovery_plan(self, analysis):
prompt = f"""
Based on this error analysis: {analysis}
Generate a recovery strategy that:
1. Minimizes data loss
2. Maintains system stability
3. Provides user feedback
4. Logs for future prevention
"""
return await self.ai_service.generate(prompt)
project-name/
├── .github/
│ ├── workflows/
│ │ ├── ci.yml # CI pipeline configuration
│ │ ├── deploy.yml # Deployment automation
│ │ └── security-scan.yml # Security scanning
│ └── PULL_REQUEST_TEMPLATE.md # PR template
├── src/
│ ├── api/
│ │ ├── controllers/ # Request handlers
│ │ ├── middleware/ # Express middleware
│ │ ├── routes/ # API routes
│ │ └── validators/ # Input validation
│ ├── services/
│ │ ├── ai/ # AI service integrations
│ │ │ ├── prompts/ # Prompt templates
│ │ │ ├── handlers/ # AI response handlers
│ │ │ └── config.js # AI service configuration
│ │ ├── database/ # Database services
│ │ ├── cache/ # Caching layer
│ │ └── monitoring/ # Logging & monitoring
│ ├── models/ # Data models
│ ├── utils/ # Utility functions
│ └── config/ # Configuration files
├── client/
│ ├── src/
│ │ ├── components/ # React/Vue components
│ │ ├── hooks/ # Custom hooks
│ │ ├── services/ # API service layer
│ │ ├── store/ # State management
│ │ └── utils/ # Frontend utilities
│ └── public/ # Static assets
├── tests/
│ ├── unit/ # Unit tests
│ ├── integration/ # Integration tests
│ ├── e2e/ # End-to-end tests
│ └── performance/ # Performance tests
├── docs/
│ ├── api/ # API documentation
│ │ └── openapi.yaml # OpenAPI specification
│ ├── architecture/ # Architecture decisions
│ ├── deployment/ # Deployment guides
│ └── user-manual/ # User documentation
├── scripts/
│ ├── setup.sh # Development setup
│ ├── deploy.sh # Deployment script
│ └── seed-data.js # Database seeding
├── docker/
│ ├── Dockerfile # Container configuration
│ └── docker-compose.yml # Multi-container setup
├── .env.example # Environment variables template
├── README.md # Project overview
├── CONTRIBUTING.md # Contribution guidelines
├── AI-INTEGRATION.md # AI usage documentation
└── package.json # Dependencies & scripts
| Criterion | Weight | Metrics |
|---|---|---|
| Code Quality | 10% | - Clean code principles applied- SOLID principles demonstrated- Design patterns used appropriately- Code review checklist passed |
| Architecture | 10% | - Scalable system design- Proper separation of concerns- Microservices/modular approach- Database optimization |
| Testing | 10% | - Unit test ``coverage > 80``%- Integration tests for all APIs- E2E tests for critical paths- Performance test results |
| Security | 10% | - OWASP top 10 addressed- Input validation implemented- Authentication/authorization- Data encryption in place |
| Criterion | Weight | Metrics |
|---|---|---|
| Prompt Engineering | 10% | - Advanced prompting techniques- Context management- Error handling for AI failures- Cost optimization strategies |
| Integration Quality | 10% | - Seamless AI/app integration- Response time optimization- Fallback mechanisms- AI monitoring implemented |
| Innovation | 10% | - Novel AI use cases- Creative problem solving- AI-human collaboration- Business value demonstration |
| Criterion | Weight | Metrics |
|---|---|---|
| Documentation | 10% | - API documentation complete- Code comments meaningful- Architecture documented- Setup guide tested |
| DevOps | 5% | - CI/CD pipeline functional- Containerization implemented- Monitoring configured- Deployment automated |
| Performance | 5% | - Load testing passed- Response `times < 500`ms- Resource optimization- Caching implemented |
| Criterion | Weight | Metrics |
|---|---|---|
| Presentation | 5% | - Professional case study- Clear value proposition- Technical depth shown- Business impact quantified |
| Employability | 5% | - Industry relevance- Transferable skills shown- Problem-solving demonstrated- Leadership potential evident |
Required Components:
Repository Standards:
AI Integration Report (3-5 pages):
API Documentation:
Demo Video Requirements (5-7 minutes):
Case Study Document:
# [Project Name] - AI Integration Case Study
## Executive Summary
[200-word overview of project impact]
## Problem Statement
[Clear definition of the problem solved]
## Solution Architecture
[Technical approach with diagrams]
## AI Integration Strategy
[Detailed explanation of AI usage]
## Results & Metrics
- Performance improvements: X%
- Cost optimization: $Y saved
- User satisfaction: Z% increase
- Development time: N hours saved
## Technical Challenges Overcome
[3-5 major challenges with solutions]
## Future Enhancements
[Roadmap for next 6 months]
## Key Learnings
[5-7 bullet points of insights gained]
Required Slides (10-12 slides):
Before submission, ensure your code passes this professional review:
## Week 1: Foundation
- [ ] Market research for chosen domain
- [ ] Competitor analysis (3-5 similar solutions)
- [ ] Technical feasibility study
- [ ] AI cost estimation model
- [ ] Risk assessment document
## Week 2: Implementation
- [ ] Daily standup logs
- [ ] Sprint planning with AI tasks
- [ ] Code review cycles
- [ ] Performance monitoring setup
- [ ] Security audit scheduling
# Example: Production-Ready Prompt Template
class PromptTemplate:
def __init__(self):
self.base_prompt = """
Context: {context}
Constraints: {constraints}
Expected Output Format: {format}
Task: {task}
Additional Requirements:
- Include error handling
- Follow {coding_standard} standards
- Add comprehensive comments
- Consider edge cases
- Optimize for {optimization_target}
"""
def generate(self, **kwargs):
return self.base_prompt.format(**kwargs)
// Example: AI Response Validator
class AIResponseValidator {
static async validate(response, schema) {
const validation = {
syntaxValid: await this.checkSyntax(response),
securityPassed: await this.securityScan(response),
performanceAcceptable: await this.performanceCheck(response),
businessLogicSound: await this.logicValidation(response, schema)
};
return {
isValid: Object.values(validation).every(v => v === true),
details: validation,
suggestions: await this.generateImprovements(validation)
};
}
}
# Branching strategy for AI-assisted development
main
├── develop
│ ├── feature/ai-integration-auth
│ ├── feature/ai-nlp-processor
│ └── feature/ai-analytics-engine
├── release/v1.0
└── hotfix/ai-response-timeout
# Commit message format
type(scope): subject
# Examples:
feat(ai): implement natural language task parser
fix(api): handle AI service timeout gracefully
docs(ai): add prompt engineering best practices
perf(cache): optimize AI response caching strategy
Your project should achieve:
This project represents your transition from learning AI-assisted development to practicing it professionally. Your goal is to create something that:
Remember: This isn't just a course project-it's your entry into the world of AI-powered software development. Make it count.