Production AI Application

Production AI Application

Purple → Brown Belt Multi-day project

ภาพรวม

ส่ง AI-powered application ที่พร้อมใช้งานใน production This capstone tests your ability to build AI systems that are secure, scalable, cost-effective, and observable.

Prerequisites

• Current Belt: Purple
• Quests Completed: At least 90 quests
• Modules Covered: Module 1-8

ข้อกำหนด

Phase 1: Architecture (Day 1-2)

• Define product requirements and success metrics
• Design system architecture with AI components
• Plan for cost management (token budgets, caching)
• Design security controls for AI interactions
• Create observability strategy

Phase 2: Core Implementation (Day 3-6)

• Build the AI-powered feature(s)
• Implement proper prompt management (versioning, testing)
• Add semantic caching to reduce costs
• Implement rate limiting and error handling
• Add input validation and prompt injection prevention

Phase 3: Production Readiness (Day 7-8)

• Comprehensive logging and monitoring
• Cost tracking and alerts
• Performance optimization
• Security audit
• Load testing

Phase 4: Deployment & Documentation (Day 9-10)

• Deploy to production
• Set up CI/CD pipeline
• Create runbook for operations
• Write comprehensive documentation
• Post-launch monitoring plan

Example Projects

Choose one or propose your own:

1. AI-Powered SaaS Feature - Add AI capabilities to an existing product
2. Customer Support Bot - Multi-turn conversation with knowledge retrieval
3. Content Generation Platform - Generate and manage AI-created content
4. Data Analysis Tool - AI-assisted data exploration and visualization

Rubric

Criteria	Weight	Description
Production Quality	30%	Actually deployed, working, handles real users
Cost Management	20%	Token budgeting, caching, cost monitoring
Security	20%	Input validation, prompt injection prevention, secrets management
Observability	15%	Logging, monitoring, alerting, debugging capabilities
Documentation	15%	Architecture docs, runbook, user docs

Getting Started

npx bluebeltdojo download capstone-3-production-ai
cd capstone-3-production-ai

Key Concepts to Demonstrate

• Prompt Engineering - Effective, tested, versioned prompts
• Cost Optimization - Semantic caching, token budgets, model routing
• Security - Input sanitization, prompt injection defense, secrets management
• Observability - LLM-specific logging, cost tracking, quality metrics
• Production Operations - Deployment, monitoring, incident response

Cost Management Checklist

• Track token usage per request
• Implement semantic caching
• Set up cost alerts
• Use appropriate models for different tasks
• Monitor and optimize prompt efficiency

Security Checklist

• Input validation on all user inputs
• Prompt injection testing
• Secrets in environment variables
• Rate limiting on AI endpoints
• Output filtering/sanitization

Tips

• This is about PRODUCTION readiness, not just features
• Cost management is critical - track everything
• Security is not optional - test for prompt injection
• Observability will save you when things go wrong
• Document your operational procedures
• Think about what happens when the AI fails