Retrieval Augmented Generation (RAG) with LangChain
DataCamp
Last updated June 30, 2026
A focused, hands-on course on retrieval-augmented generation — the technique that fixes a core weakness of large language models, their fixed and limited knowledge, by feeding them relevant external data at query time. Working in Python with LangChain, you go past a basic RAG setup into the methods that make it work well: splitting documents in ways that respect their structure, including syntax-aware splitting of code files, splitting by tokens to stay within a model's context window, and splitting at points where the subject changes. You then evaluate RAG systems rigorously using LangSmith and Ragas, and meet the Graph RAG architecture. You finish able to build and assess a more robust retrieval system. This is a coding course built on Python and LangChain.
What you'll learn
- Why retrieval-augmented generation extends an LLM's limited knowledge
- Building a RAG application with LangChain
- Structure-aware document splitting: syntax, tokens, and topic shifts
- Staying within a model's context window
- Evaluating RAG with LangSmith and Ragas, plus the Graph RAG architecture
Frequently asked questions about Retrieval Augmented Generation (RAG) with LangChain
Who is Retrieval Augmented Generation (RAG) with LangChain for?
Developers who know the basics of LLM applications and RAG and want to build more robust, well-evaluated retrieval systems.
Is Retrieval Augmented Generation (RAG) with LangChain free?
No — Retrieval Augmented Generation (RAG) with LangChain is a paid course.
What are the prerequisites for Retrieval Augmented Generation (RAG) with LangChain?
Prior LangChain application experience (e.g. Developing LLM Applications with LangChain).
Do you need to code for Retrieval Augmented Generation (RAG) with LangChain?
Yes — Retrieval Augmented Generation (RAG) with LangChain involves hands-on coding.
Does Retrieval Augmented Generation (RAG) with LangChain offer a certificate?
Yes. DataCamp Statement of Accomplishment on completion (requires DataCamp Premium).
Why we suggest this course
A deeper, technique-focused course for developers who already know the basics of RAG and want to make it actually reliable. The attention to how documents are split — by syntax, by tokens, by topic — and to rigorous evaluation with LangSmith and Ragas is where naive RAG implementations usually fall down, so this targets the hard part. It builds directly on prior LangChain application work, making it a step up rather than an entry point. You can sample the opening chapter before subscribing; the full course and its Statement of Accomplishment are part of DataCamp Premium.