Path 3: Build AI Products

Design, build, and deploy production-ready AI-powered applications.

Path 2 Completion Checklist

Before starting Path 3, ensure you understand:

• ✅ Built a streaming chatbot with error handling
• ✅ Implemented RAG search with embeddings
• ✅ Used tool calling to create interactive agents
• ✅ Deployed an AI feature to production

Need to review? → Path 2: Integration

Before You Start

Preparing for Production? Review these engineering pillars:

• Testing AI Systems - Reliability and quality assurance
• Observability - Monitoring and debugging in production
• Security Best Practices - Protecting keys and user data

Choose Your Learning Style

• 🎯 Learn by Doing: Start with Full-Stack SaaS project → reference guides
• 📚 Learn by Content: Read Advanced RAG Patterns first → then build

Path 3: Build AI Products

Design, build, and deploy production-ready AI-powered applications.

Week 1

Advanced RAG

Implement production-grade RAG with hybrid search, re-ranking, and metadata filtering for enterprise knowledge bases.

RAG PatternRAG Search ProjectLocal Embeddings

Week 2

AI Engineering

Treat prompts like code. Implement evals, monitoring, and feedback loops to systematically improve AI quality.

TestingObservabilityEvalsSecurity

Week 3

Deployment & Optimization

Deploy to Edge/Workers for low latency, implement caching strategies, rate limiting, and cost optimization.

Vercel EdgeCloudflare WorkersCachingCost Optimization

Week 4

Multi-Agent Systems

Build complex autonomous workflows with multiple specialized agents, fine-tuning strategies, and local LLM deployment.

Multi-Agent AppAgent PatternsTraining OverviewBrowser AI

Next Steps

Ready to build? → Projects Section

Review concepts? → Tech Fundamentals