Advance RAG Patterns is an intermediate course designed for AI developers and ML engineers who have built a basic RAG pipeline but find it still fails on complex or nuanced queries. While foundational RAG reduces hallucinations, production-grade AI demands greater reliability, accuracy, and reasoning. This 2-hour course moves beyond the basics to teach you how to engineer robust, intelligent, and self-correcting systems.

Focused on practical, job-ready skills, this course dives deep into cutting-edge architecture. You will learn to implement and evaluate a suite of advanced patterns, including Corrective RAG for query rewriting, Self-RAG for source validation, and Agentic RAG for multi-hop problem-solving. Through hands-on, in-browser projects, you will A/B test these different architectures, analyze their performance against key metrics, analyze different embedding services, and make data-driven decisions on improving accuracy. By the end, you'll be able to not just build, but architect and defend production-ready RAG systems that are both powerful and trustworthy.