Follow the weeks in order or use them as modules at your own pace. Each lecture has a reading view and its original slide deck.
Suggested practice below supports independent study. Check MCP version compatibility before the server exercises.
Foundations and reasoning
| Week | Topic and suggested practice | Materials |
|---|---|---|
| Week 1 | The AI model landscapeReasoning models, inference-time compute, model selection, and the limits of benchmarks. Suggested practiceCompare two available models on the same task. Record model versions, outputs, latency, and the limits of your comparison. | NotesSlides |
| Week 2 | Prompt engineering and structured outputsPrompt design, structured responses, tool use, prompt versioning, and reliability. Suggested practiceCreate a versioned prompt with a structured output schema and an evaluation set. Compare it with a simple baseline. | NotesSlides |
| Week 3 | The AI developer stackFramework choices, agent loops, observability, error handling, and deployment patterns. Suggested practiceBuild a tool-using agent with two tools, structured output, and at least 10 scored evaluation cases. The lecture uses PydanticAI. | NotesSlides |
| Week 4 | Knowledge representationEmbeddings, vector search, hybrid retrieval, knowledge graphs, and context design. Suggested practiceIndex a small corpus. Compare keyword, vector, and hybrid retrieval on the same hand-labeled questions. | NotesSlides |
Knowledge augmentation and evaluation
| Week | Topic and suggested practice | Materials |
|---|---|---|
| Week 5 | Retrieval-augmented generationChunking, retrieval, reranking, grounding, and iterative retrieval workflows. Suggested practiceBuild a RAG baseline with citations. Track retrieval failures separately from answer-generation failures. | NotesSlides |
| Week 6 | GraphRAG and global contextGraph-based retrieval, community summaries, global questions, and evaluation tradeoffs. Suggested practiceCompare graph-based retrieval with the Week 5 baseline on both local and corpus-wide questions. | NotesSlides |
| Week 7 | Evaluation and LLM-as-a-judgeEvaluation layers, RAG metrics, judge biases, calibration, and human review. Suggested practiceEvaluate two retrieval or prompt variants on 50 questions. Report faithfulness, relevance, context precision, and context recall. | NotesSlides |
MCP and agentic systems
| Week | Topic and suggested practice | Materials |
|---|---|---|
| Week 8 | Model Context ProtocolTools, resources, prompts, transport, authorization, and host/server responsibilities. Suggested practiceInspect a tool call and document its schema, authorization requirements, and failure behavior. Identify the protocol version used. | NotesSlides |
| Week 9 | Building MCP serversServer implementation, validated tool handlers, testing, remote deployment, and operational safety. Suggested practiceImplement a small MCP server against a disposable data source. Test invalid inputs, rejected access, and real tool calls. | NotesSlides |
| Week 10 | Multi-agent workflowsCoordination patterns, shared state, tool isolation, cost controls, and failure recovery. Suggested practiceCompare a single-agent baseline with one coordinated workflow. Measure task completion, latency, and cost. Draft the capstone proposal. | NotesSlides |
| Week 11 | Human oversight and memoryApproval gates, escalation, persistent state, memory retrieval, and privacy. Suggested practiceAdd an approval gate and durable state to a workflow. Test rejected actions, restart behavior, and deletion of stored information. | NotesSlides |
Efficiency and governance
| Week | Topic and suggested practice | Materials |
|---|---|---|
| Week 12 | Small models and model routingTask-specific benchmarking, quantization, serving, hardware limits, and model cascades. Suggested practiceBenchmark three model sizes on a 200-example task set and plot cost against quality. Collect the capstone's first evaluation results. | NotesSlides |
| Week 13 | Distillation and fine-tuningSynthetic data, LoRA, QLoRA, preference tuning, evaluation, and data provenance. Suggested practiceDesign a small fine-tuning experiment with a held-out test set and documented data permissions. Compare with the untuned baseline. | NotesSlides |
| Week 14 | Safety and red-teamingPrompt injection, agent hijacking, guardrails, least privilege, and adversarial evaluation. Suggested practiceAdd layered controls to a chatbot or agent. Test both blocked attacks and legitimate tasks; record failures and freeze the capstone demo. | NotesSlides |
Capstone
| Week | Topic and suggested practice | Materials |
|---|---|---|
| Week 15 | Capstone: build, measure, explainA working AI system, architecture writeup, measured evaluation, and an honest failure analysis. Suggested practiceDeliver reproducible code, a 15-minute demonstration, a 4–6 page writeup, and evaluation results. Use the published capstone rubric for self-assessment. | NotesSlides |