A 40–50 hour code-first program for developers, engineers, and tech leads. Build, integrate, and deploy AI-enabled applications and agentic workflows — safely and responsibly, without needing enterprise-scale architecture.
Purpose: Give developers enough LLM understanding to build reliable applications — not train models.
A. Core LLM Concepts
B. Model Selection Framework
C. Multi-Modal Models — Apps That See, Hear & Read
Purpose: Learn to build real AI-powered applications — not just write prompts.
A. Prompt Engineering for Production
B. Tool Calling & Function Execution
C. Error Handling & UX Patterns
D. PromptOps — Managing Prompts Like Code
Purpose: Connect AI with real data and documents so outputs are accurate, reliable, and verifiable — not hallucinated.
A. Core Concepts
B. Building RAG Systems
C. Hands-On Labs
Purpose: Build AI systems that can plan, use tools, take actions, and stay controlled in real-world applications.
A. What You Learn
B. Controlled Agent Design
C. Hands-On Labs
Purpose: Ensure AI systems are accurate, reliable, and production-ready — not just working in demos.
A. Why Evaluation Matters
B. What You Measure
C. Evaluation in Practice
Purpose: Build, present, and defend a deployable or demo-ready AI system.
A. Choose Your Track
B. Capstone Must Demonstrate




Purpose: Give developers enough LLM understanding to build reliable applications — not train models.
A. Core LLM Concepts
B. Model Selection Framework
C. Multi-Modal Models — Apps That See, Hear & Read
Purpose: Learn to build real AI-powered applications — not just write prompts.
A. Prompt Engineering for Production
B. Tool Calling & Function Execution
C. Error Handling & UX Patterns
D. PromptOps — Managing Prompts Like Code
Purpose: Connect AI with real data and documents so outputs are accurate, reliable, and verifiable — not hallucinated.
A. Core Concepts
B. Building RAG Systems
C. Hands-On Labs
Purpose: Build AI systems that can plan, use tools, take actions, and stay controlled in real-world applications.
A. What You Learn
B. Controlled Agent Design
C. Hands-On Labs
Purpose: Ensure AI systems are accurate, reliable, and production-ready — not just working in demos.
A. Why Evaluation Matters
B. What You Measure
C. Evaluation in Practice
Purpose: Build, present, and defend a deployable or demo-ready AI system.
A. Choose Your Track
B. Capstone Must Demonstrate



