Course Description

Master next-gen AI systems with “Agentic RAG using LangGraph,” a hands-on course for building advanced, modular retrieval-augmented generation (RAG) pipelines. You'll explore cutting-edge agentic architectures like Router RAG, Corrective RAG, and Adaptive RAG, and learn how to design intelligent, self-improving agents using LangGraph. Through real-world projects and step-by-step guidance, you’ll gain the skills to create scalable, flexible AI systems that plan, retrieve, and reason effectively. Perfect for developers and AI practitioners ready to move beyond basic RAG.

Course curriculum

  • 1
    Introduction to Agentic RAG and LangGraph
    • Course Introduction
    • Recap of RAG Systems
    • Recap of AI Agents
    • What is Agentic RAG
    • Recap of the LangGraph Agentic AI Framework
    • Quiz
    • Course Handouts
  • 2
    Popular Agentic RAG Architectures
    • Overview of Popular Agentic RAG System Architectures
    • Understanding Router Agentic RAG Systems
    • Understanding Query Planner RAG Systems
    • Understanding Agentic Corrective RAG Systems
    • Understanding Self-Reflective RAG Systems
    • Understanding Adaptive RAG Systems
    • Understanding Speculative RAG Systems
    • Understanding Self-Route RAG Systems
    • Quiz
  • 3
    Project: Build a Router RAG System
    • Project Introduction & Architecture
    • Implementation - Part I - Project Setup
    • Implementation - Part II - Understand Agent Graph Workflow
    • Implementation - Part III - Create Node Functions - I
    • Implementation - Part IV - Create Node Functions - II
    • Implementation - Part V - Create Node Functions - III
    • Implementation - Part VI - Create Node Functions - IV
    • Implementation - Part VII - Build & Test Agent
    • Quiz
  • 4
    Project: Build an Agentic Corrective RAG System
    • Project Introduction & Architecture
    • Implementation - Part I - Project Setup
    • Implementation - Part II - Build Agent Component Workflows
    • Implementation - Part III - Understand Agent Graph Workflow
    • Implementation - Part IV - Create Node Functions
    • Implementation - Part V - Build Test Agent
    • Quiz
  • 5
    Project: Build an Adaptive RAG System
    • Project Introduction & Architecture
    • Implementation - Part I - Project Setup
    • Implementation - Part II - Build Agent Component Workflows - I
    • Implementation - Part III - Build Agent Component Workflows - II
    • Implementation - Part IV - Understand Agent Graph Workflow
    • Implementation - Part V - Create Node Functions
    • Implementation - Part VI - Build Agent
    • Implementation - Part VII - Test Agent
    • Quiz

Who Should Enroll

  • AI/ML Engineers looking to build modular, agent-based RAG systems using LangGraph.

  • Developers ready to go beyond traditional pipelines and experiment with adaptive, corrective, and self-reflective agents.

  • Data Scientists aiming to enhance their retrieval workflows with structured agent control and intelligent routing.

Key Takeaways

Understand the fundamentals of agentic RAG and how it extends traditional RAG systems.

  • Understand the fundamentals of agentic RAG and how it extends traditional RAG systems.

  • Learn the LangGraph framework and how to build structured, modular agent workflows.

  • Explore cutting-edge architectures like Router RAG, Corrective RAG, Adaptive RAG, and more.

  • Gain hands-on experience building real-world RAG systems from the ground up.

About the Instructor

Dipanjan Sarkar - Head of Community and Principal AI Scientist, Analytics Vidhya

Dipanjan Sarkar is a distinguished Lead Data Scientist, Published Author, and Consultant, having a decade of extensive expertise in Machine Learning, Deep Learning, Generative AI, Computer Vision, and Natural Language Processing. His leadership spans Fortune 100 enterprises to startups, crafting end-to-end data products and pioneering Generative AI upskilling programs. A seasoned mentor, Dipanjan advises a diverse clientele, from novices to C-suite executives and PhDs, across Advanced Analytics, Product Development, and Artificial Intelligence. His recognitions include "Top 10 Data Scientists in India, 2020," "40 under 40 Data Scientists, 2021," "Google Developer Expert in Machine Learning, 2019," and "Top 50 AI Thought Leaders, Global AI Hub, 2022," alongside global accolades and a Google Champion Innovator title in Cloud AI/ML, 2022.
About the Instructor

FAQ

  • Do I need prior experience with LangGraph?

    No. The course starts with a full introduction to LangGraph and guides you through its core concepts and usage step by step.

  • What programming background is required?

    Basic knowledge of Python is recommended, as the course involves writing node functions and agent workflows using Python.

  • What types of RAG architectures will I learn?

    You'll explore and build multiple architectures including Router RAG, Corrective RAG, Adaptive RAG, and more.

  • Is this course project-based?

    Yes. Each module includes a hands-on project where you implement an agentic RAG system from scratch.

  • Can I apply these skills in real-world or enterprise settings?