Agentic RAG Fundamentals: Build AI Agents with LangGraph

As artificial intelligence evolves, standard retrieval models are no longer enough. Modern applications require agentic workflows—systems that can evaluate, route, and self-correct their own answers for maximum accuracy. This course provides a clear, text-based introduction to building Agentic RAG (Retrieval-Augmented Generation) systems. You will start with the fundamental concepts of language models and vector databases before moving into the architecture of self-correcting agents. By reading through practical explanations and code snippets, you will understand how to orchestrate complex AI workflows using LangGraph. What you'll learn: - Understand the foundational concepts of RAG, vector databases, and prompt engineering. - Learn how agentic workflows differ from standard linear AI generation. - Build self-correcting retrieval loops that evaluate and refine their own outputs. - Apply LangGraph to orchestrate stateful, multi-step AI processes. - Practice designing logic flows for document retrieval, grading, and generation. The material is structured to build your knowledge step-by-step. We begin with core AI terminology and basic retrieval patterns, gradually introducing state management and self-reflection loops. This course is designed for beginners and developers new to AI agents—no prior experience with LangGraph or advanced machine learning is required. Start reading today to unlock the next level of intelligent application development.