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Lessons
Nik Reljin edited this page Jun 13, 2026
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1 revision
For interactive step-by-step versions of each lesson, see the course site.
| # | Lesson | Run | Read | Live | Status |
|---|---|---|---|---|---|
| 1 | RAG from scratch — document Q&A with BM25 + embeddings + citations | ./run -l 1 |
LESSON1.md | open | Available |
| 2 | MCP servers — expose search as a Model Context Protocol tool | ./run -l 2 |
LESSON2.md | open | Available |
| 3 | LangChain — rebuild RAG with LangChain, compare trade-offs | — | LESSON3.md | — | Planned |
| 4 | LangGraph — stateful, self-correcting agent graph | — | LESSON4.md | — | Planned |
| 5 | Ollama + Function Calling — local model with real tools, offline | — | LESSON5.md | — | Planned |
| 6 | Semantic Kernel — agent in C# / .NET with SK plugins | — | LESSON6.md | — | Planned |
| 7 | AWS Bedrock Agents — knowledge bases + action groups on cloud | — | LESSON7.md | — | Planned |
| 8 | Google AI Development Kit — Gemini agent with Google's ADK | — | LESSON8.md | — | Planned |
New lessons focused on:
- Testing AI systems — unit testing prompts, evaluating retrieval quality, regression testing LLM outputs
- Developer-oriented AI tooling — prompt engineering for devs, AI-assisted code review, CI/CD integration
All printable PDFs are in docs/pdf/ on the course site.