Retrieval Augmented Generation (RAG) with LangChain
Add reliable retrieval to your LLMs with LangChain
Retrieval Augmented Generation (RAG) with LangChain teaches developers how to extend large language models by integrating external data through techniques such as vector retrieval and graph-based approaches.
Designed for engineers and technical practitioners familiar with LangChain, the course emphasizes practical workflows for connecting embeddings, vector stores, and prompt templates so LLM responses become more accurate and use-case specific.
At a Glance
Retrieval Augmented Generation (RAG) with LangChain is a DataCamp course that explains how to integrate external data sources into large language models using LangChain, covering both vector-based retrieval and graph-based alternatives.
It is offered on DataCamp and taught by the platform’s instructors for software engineers and developers familiar with LangChain, with a strong emphasis on practical implementation and retrieval strategies.
| Level | Intermediate |
| Rating | 4.8 out of 5 |
| Duration | 2+ hours |
| Languages | English |
| Certificate | Statement of accomplishment |
| Access | Access for as long as your subscription is active |
| Course includes |
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| Price | Included with subscription |
What This Course Teaches
This course frames outcomes as practical, measurable competencies that prepare learners to extend LLMs with external data and retrieval strategies.
By the end, learners should be able to implement end-to-end RAG pipelines with LangChain, apply vector and graph retrieval methods, and evaluate retrieval effectiveness in application contexts.
How the Course Is Structured
The course is organized into four concise sections that progress from prerequisites through practical RAG implementations to graph-based alternatives.
Overall it runs about 2+ hours in total and is delivered as a short, modular sequence of focused lessons aimed at developers already familiar with LangChain.
Curriculum overview
01 Prerequisites ▾
Lists recommended prior learning and resources, including the “Developing LLM Applications with LangChain” course as preparation.
02 Building RAG Applications with LangChain ▾
Covers vector-based retrieval workflows and how to connect vector stores to LLM prompts using LangChain primitives.
03 Improving the RAG Architecture ▾
Explores enhancements to core RAG patterns, including retriever and prompt refinements and architecture-level trade-offs.
04 Introduction to Graph RAG ▾
Introduces graph-based RAG approaches that transform text into nodes and relationships and query them to improve retrieval fidelity.
Audience & Requirements
This course is intended for developers and technical practitioners who want to add retrieval capabilities to LLM-powered applications; it assumes some prior exposure to LangChain and applied LLM concepts.
Ideal learners are engineers and technical builders who will implement or evaluate RAG pipelines in production or prototypes.
- Software engineers building LLM-powered features or assistants.
- Machine-learning engineers or ML-focused developers adding retrieval to models.
- Backend or infrastructure engineers integrating vector stores and retrieval services.
- Technical product builders prototyping knowledge-augmented applications.
- Familiarity with using LangChain (explicitly expected by the course).
- Working knowledge of large language model concepts (prompts, tokens, biases).
- Basic Python programming experience sufficient to run examples and scripts.
- Familiarity with embeddings or vector stores (helpful for following practical sections).
Final Verdict
For technical practitioners who already use LangChain, this short course is a pragmatic, well-regarded way to add retrieval-augmented techniques to real applications.
Because it is offered through the platform’s subscription model and issues a statement of accomplishment, it represents good value for developers who regularly use the service or plan multiple related courses; however, complete beginners should prepare with foundational LangChain material before enrolling.
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