Course Lessons
Master AI development from fundamentals to advanced production systems
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0. Module 0: Orientation
Two out of three isn't good enough - internalize Prompt + Model + Objective alignment
1. Module 1: Pick a Problem Worth Solving
Objective first - outcome framing, user stories, scoping, and defining success
2. Module 2: Prompt Engineering as Product Design
Prompt patterns, constraints, structured outputs, testing, and prompt libraries
3. Module 3: Models - Choosing the Right Brain
Closed vs open, local vs hosted, latency/cost/privacy tradeoffs, multimodal basics
4. Module 4: Make It Useful - RAG & Knowledge
Embeddings, chunking, retrieval quality, citations, grounding - the AI librarian pattern
5. Module 5: Tools and Tool-Calling
Function calling, tool contracts, retries, bridging to agents
6. Module 6: Chatbots vs Task Agents
Conversational UX vs execution pipelines - when to use each
7. Module 7: Multi-Agent Tribe Design
Roles, delegation, coordination, memory boundaries, agent clusters
8. Module 8: Shipping - Prototype to Product
SDLC for AI apps, PRDs that matter, iteration loops, demo-driven development
9. Module 9: Evals, Reliability & Take the Gain
Eval harnesses, golden sets, regression testing, automate to 80% then use your brain
10. Module 10: Production Readiness
Security, monetization basics, what to do before charging money
11. Module 11: Protocols & Interoperability
MCP, A2A, GenUI, WebLLM - building AI systems that work together