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• Explain what RAG is, why LLMs need it, and when retrieval is the right fix for hallucinations and outdated knowledge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.

Who this course is for

Python developers who want their agent to answer from their own documents instead of guessing. AI engineers building assistants that need current, verifiable knowledge. Developers who have made a basic "chat with your documents" demo and want to understand what is actually happening under the hood. Basic familiarity with LangChain is enough; exposure to LangGraph helps but is not required.

Why take it

It is short and practical: two theory sections, two builds, and a pipeline that works with any documents you swap in. You will be able to diagnose a bad answer instead of just rewriting the prompt.

Enroll now and build an agent that answers from your data.