Building Agentic and Modular RAG Systems with LangGraph

Standard retrieval systems often fail when queries are complex or documents are messy. By transitioning to agentic, modular RAG, you can build AI applications that evaluate their own search results, correct errors, and route queries dynamically. This text-only course guides you through the process of designing and implementing advanced retrieval architectures. You will transition from simple document lookup to building autonomous, state-driven AI agents using LangGraph. What you'll learn: - Understand the core concepts of agentic RAG and how it improves upon traditional retrieval. - Configure state graphs, nodes, and conditional routing using LangGraph. - Implement corrective RAG patterns to evaluate and filter retrieved documents. - Apply query rewriting and fallback mechanisms for more accurate LLM responses. - Design modular workflows that handle complex, multi-step user queries. - Practice building structured tool-calling loops within a stateful system. The course starts with essential terminology and foundational RAG concepts before moving step-by-step through graph states, routing logic, and error-correction loops. You will read clear explanations and analyze structured code snippets designed to help you construct robust AI workflows. This program is designed for developers and AI enthusiasts eager to build next-generation search applications, with no prior LangGraph experience required. Start reading today to build smarter, self-correcting AI systems.