Master the Mechanics of RAG: A Hands-On Build Lab

Most AI tutorials teach you how to call an API. They don’t teach you how to build systems that return reliable answers from your own data. This workshop is different. We’ll go beyond “chat with your docs” demos and build a production-grade Retrieval-Augmented Generation (RAG) system—one you can actually debug, evaluate, and improve. This is a hands-on, high-intensity session for developers and architects who want to understand how these systems really work. 🛠 What You’ll Learn (and Build) We’ll break down RAG into the core components that actually matter: End-to-End Architecture From ingestion → chunking → retrieval → generation → evaluation Smart Chunking Why naive chunking fails—and how to structure data for better retrieval Advanced Retrieval Strategies Move beyond “top-k similarity” to get relevant, grounded answers Evaluation That Isn’t Guesswork How to measure answer quality instead of relying on vibes Observability & Debugging How to see where your pipeline breaks—and fix it Hands-On Implementation Build a working RAG pipeline step-by-step 🎯 Who This Is For Frustrated Builders Your RAG app works… sometimes Engineers & Developers You know Python/JS and LLM basics, but want real systems thinking Solution Architects You need to evaluate trade-offs across vector DBs, embeddings, and retrieval Career Pivoters Move from prompt engineering → building full AI pipelines 📦 What You’ll Walk Away With A working RAG boilerplate you can reuse A reference architecture you can explain and defend A mental model for debugging poor outputs A framework for improving answer quality and performance Bring your laptop. Let’s stop “chatting” with AI—and start building systems that actually work. “Toronto Tech Week is a citywide celebration of the people building what’s next. From May 25–29, 2026, founders, investors, and builders come together for hundreds of community-led events across Toronto, connecting tens of thousands of people around Canadian tech. Torontotechweek.com”