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Hands-On RAG Bootcamp: Build Apps with LangGraph & LangChain
Rating: 4.2 out of 5(5 ratings)
45 students
Created byMuhammad Moin
Last updated 11/2025
English

What you'll learn

  • Getting Started with Retrieval-Augmented Generation (RAG)
  • Create a Simple RAG Application using LangChain
  • Build a RAG System to Chat with Multiple PDF Documents
  • Build a RAG Application from Scratch — No LangChain, No LlamaIndex
  • Conversational RAG with LangChain: Memory and Multi-Turn Logic
  • Build a Conversational RAG Streamlit App with Chat History using LangChain
  • Multimodal RAG: Chat with Complex PDFs (Text, Tables & Images)
  • Conversational Multimodal RAG: Chat with Complex PDFs (Text, Images & Tables)
  • Build a Hybrid CSV Intelligence Agent Using RAG, Pandas, and LLM Judge
  • Getting Started with Agentic RAG: Step-by-Step Implementation Using LangGraph

Course content

10 sections • 10 lectures • 4h 50m total length

  • Getting Started with Retrieval-Augmented Generation (RAG)15:20

    This lecture introduces Retrieval-Augmented Generation (RAG), explaining what it is and how it works.

Requirements

  • Basic understanding of Python programming
  • No prior knowledge of RAG is required —we’ll start from scratch
  • Basic knowledge of LangChain is a plus

Description

Unlock the Power of RAG – From Basic to Advanced AI Systems

RAG (Retrieval-Augmented Generation) is a powerful AI technique that helps systems understand, retrieve, and generate information intelligently. It is used in chatbots, virtual assistants, research tools, and enterprise AI solutions. This course will guide you step by step, from building simple RAG pipelines to creating advanced Agentic AI systems, giving you practical skills to apply RAG in real-world projects.

The Hands-On RAG Bootcamp is your step-by-step guide to learning RAG with LangChain and LangGraph. Whether you’re new to AI or an experienced developer, this course will take you from the basics to advanced Agentic RAG systems.

What You Will Learn:

  • Getting Started with Retrieval-Augmented Generation (RAG)

  • Create a Simple RAG Application using LangChain

  • Build a RAG System to Chat with Multiple PDF Documents

  • Build a RAG Application from Scratch — No LangChain, No LlamaIndex

  • Conversational RAG with LangChain: Memory and Multi-Turn Logic

  • Build a Conversational RAG Streamlit App with Chat History using LangChain

  • Multimodal RAG: Chat with Complex PDFs (Text, Tables & Images)

  • Conversational Multimodal RAG: Chat with Complex PDFs (Text, Images & Tables)

  • Build a Hybrid CSV Intelligence Agent Using RAG, Pandas, and LLM Judge

  • Getting Started with Agentic RAG: Step-by-Step Implementation Using LangGraph


Who this course is for:

  • Anyone who wants to learn RAG, from basics to advanced systems
  • AI beginners who want a step-by-step, hands-on way to learn RAG
  • Anyone who aims to build smart assistants, chatbots, or research tools

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