Udemy – RAG Agents with LangChain & LangGraph: A Practical Guide

Ask an LLM about a document written yesterday and you get a confident, detailed, wrong answer. The model only knows what it saw in training. Retrieval-Augmented Generation fixes this by letting the agent look things up before it responds, and this course teaches you to build a RAG agent that answers from your own data.

What you will learn:

• Explain what RAG is, why LLMs need it, and when retrieval is the right fix for hallucinations and outdated knowledge
• Understand how chunking, embeddings, retrieval, and grounding work together and how each affects answer quality
• Build a complete RAG agent in LangChain, from loading documents to a source-based answer
• Rebuild the same agent as a LangGraph workflow, with retrieval as an explicit, controllable step
• Choose between a plain LangChain implementation and a graph-based design for your own project

Inside the course

After a short grounding in agent fundamentals, the course explains the full RAG pipeline in plain terms before any code is written. Then come two hands-on builds: the same RAG agent first in LangChain, then rebuilt in LangGraph. Building it twice is deliberate: you will understand both implementations well enough to pick the right one for your needs. Written references close each section.