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Pre-Week — Kickoff Session
Onboarding, dev environment setup, tools, repos, API keys, Python environment, and your first LLM call. Live session before Week 1 starts.
Lecture — How LLMs actually work
Tokens, context windows, temperature, system prompts, model families (GPT-4o, Claude, Gemini). Understanding what you're building on.
Lecture
Lab — Build a prompt-powered CLI tool
Call the Anthropic and OpenAI APIs, chain prompts, handle structured JSON output, basic error handling. Graded submission.
Graded lab
Learning outcomes
- Understand how LLMs process tokens and use context windows
- Call major LLM APIs from Python or TypeScript
- Design effective system prompts and handle structured outputs
- Build and submit a working prompt-powered CLI application
Lecture — How RAG works end-to-end
Embeddings and semantic search, vector databases (ChromaDB, Pinecone), chunking strategies and tradeoffs, the retrieve-augment-generate pipeline.
Lecture
Lab + Project — Build a RAG app over your own documents
Ingest PDFs → embed → store in a vector DB → retrieve → answer questions. Full pipeline. Graded project submission.
Graded project
Learning outcomes
- Implement a complete RAG pipeline from ingestion to answer
- Choose and justify chunking strategies for different document types
- Set up and query a vector database (ChromaDB or Pinecone)
- Ship a working RAG application over a real document set
Lecture — Agents, tool calling & MCP
ReAct agentic loops, function/tool calling, what the Model Context Protocol is and why it matters, connecting MCP servers to agents.
Lecture
Demo — Kiro: spec-driven development & agentic IDEs
Live instructor demo of Kiro IDE — spec-based development, hooks, connecting MCP servers in an agentic IDE environment.
Live demo
Lab + Project — Build an agent with real tools via MCP
Wire an LLM agent to external tools using the Model Context Protocol. Graded submission. Optional: explore in Kiro using your 500-credit free trial.
Graded project
Learning outcomes
- Implement an agentic ReAct loop with tool calling
- Connect and use MCP servers to extend agent capabilities
- Understand spec-driven development and agentic IDEs
- Ship a working agent application that calls real external tools
Lecture — Building production-grade AI apps
LangGraph orchestration, evaluation frameworks, observability and tracing, cost control, multi-tenant architecture patterns.
Lecture
Lab — Assemble a full AI application
Combine your RAG pipeline + agent + MCP tools into one working application. Add an evaluation harness. Ungraded practice lab.
Ungraded lab
Learning outcomes
- Orchestrate multi-step AI workflows using LangGraph
- Write and run evaluations on AI application outputs
- Instrument an AI app for cost and latency observability
- Assemble a full application combining RAG, agents, and tools
Lecture — Deploying AI apps to production
Containerisation, Railway/Render deployment, API wrapping, structured logging, cost monitoring, production readiness checklist.
Lecture
Capstone Demo Day — Live presentation to the mentors
Present your deployed capstone project live. Walk through architecture decisions, defend tradeoffs, receive detailed expert feedback. Certificate awarded on completion.
Capstone
Learning outcomes
- Deploy a containerised AI application to a cloud platform
- Set up logging, cost monitoring, and basic alerting
- Present and defend architectural decisions to a technical audience
- Graduate with a live, publicly accessible AI project in your portfolio
Also included with every cohort
- Weekly Q&A with mentors
- TA office hours (async)
- Guest tech talks from industry
- Career & LinkedIn session
- Private Discord community
- Anthropic API credits for labs
- All sessions recorded
- AiBricks completion certificate
INSTRUCTORS
Reza Ahmadi, PhD
Principal AI Engineer · Professor, Queen's University
15+ years industry experience · 200+ professionals trained
Sami Riaz
AI Engineer in Industry · McGill University Alumni
7 years professional software engineering experience