Applied RAG & Enterprise Search

Who this course is for

If you've built a basic RAG demo — embed some documents, query a vector store, stuff the results into a prompt — and want to know what actually changes when that has to serve real enterprise data with real access control requirements, this course is the next step. It builds on the AI Fundamentals roadmap, so comfort with embeddings and how retrieval- augmented generation works at a basic level is assumed.

What you'll actually build

The course works through five problems that a toy RAG demo never has to solve, in order: advanced chunking and embedding strategies that hold up on messy real-world documents, hybrid search that combines vector and keyword retrieval with re-ranking to actually improve precision, multi- tenant retrieval with per-tenant access control (so one customer's search never surfaces another customer's data), evaluating and continuously tuning retrieval quality instead of shipping once and hoping, and scaling the whole system for enterprise data volumes. Every module pairs a concepts lesson with a hands-on build against a realistic dataset.

The capstone

You finish by building an enterprise knowledge-search system with access control — a system that actually enforces who can see what, not a single- tenant demo. This is the kind of project meant to go directly into a portfolio, built to be explained decision by decision in an interview.

Why this matters right now

Almost every company experimenting with AI search has hit the same wall: a retrieval demo that worked on ten sample documents falls apart at real scale, with real access-control requirements, and with retrieval quality that needs active tuning rather than a one-time setup. This course is built specifically around the parts that separate a demo from something you could actually put in front of enterprise customers.