RAG Agents with LangChain & LangGraph: A Practical Guide

Course Overview

This course teaches how to build Retrieval-Augmented Generation (RAG) agents that ground AI responses in real data. You will build a complete RAG agent using LangChain and then rebuild it as a LangGraph workflow, gaining practical skills in retrieval, chunking, embeddings, and grounding to improve answer quality and reduce hallucinations.

The course covers agent fundamentals, the full RAG pipeline, and hands-on builds to help you understand both LangChain and LangGraph implementations and choose the right approach for your projects.

Key Takeaways

• Build a complete RAG agent in LangChain that retrieves relevant documents and generates source-based answers.
• Rebuild the same RAG agent as a LangGraph workflow with explicit retrieval steps.
• Understand how chunking, embeddings, retrieval, and grounding affect answer quality.
• Explain what RAG is, why it is needed for LLMs, and when retrieval fixes hallucinations and outdated knowledge.
• Decide where retrieval fits in an agent’s reason–act–observe loop.
• Compare LangChain and graph-based RAG implementations to choose the best for your project.
• Recognize common failure modes in agent projects and apply design habits to prevent them.

Prerequisites

• Working knowledge of Python (functions, classes, lists, dictionaries).
• Basic familiarity with LangChain, including calling a chat model and defining a tool.
• Some exposure to LangGraph is helpful but not required.
• API key for an LLM provider (e.g., OpenAI, Anthropic) and access to an embedding model.
• Computer with Python 3.10+ and a code editor installed.

Target Learners

• Python developers wanting agents that answer from their own documents.
• AI engineers building assistants needing current, verifiable knowledge.
• Developers who have built basic document chat demos and want deeper understanding.
• Backend engineers grounding LLM output in internal data like docs or knowledge bases.
• Anyone seeking a practical introduction to RAG before advanced courses.