Advanced RAG Patterns

Overview

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.

Syllabus

Enhancing RAG with Correction and Validation Patterns

In this module, you will learn how to build pipelines that self-correct. You will implement patterns like Corrective RAG and Self-RAG to improve retrieval relevance and validate information before it ever reaches the user, leading to more accurate and trustworthy answers.

Agentic RAG and Performance Evaluation

In this module, you will learn to build truly intelligent systems by converting your RAG pipeline into a tool for a smart agent. You will construct an Agentic RAG system that can break down complex problems and use your retriever to find answers. Finally, you will learn to A/B test your different patterns and produce a data-driven report to prove their value.