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Fundamentals of Retrieval-Augmented Generation with LangChain
Explore this beginner RAG course to learn the basics of retrieval-augmented generation. For hands-on practice, build RAG pipelines using LangChain and create user-friendly applications with Streamlit.
4.6
21 Lessons
3h
Updated today
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- A clear understanding of the basics of retrieval-augmented generation (RAG)
- Practical experience implementing RAG pipelines using LangChain
- The ability to build a frontend application for your RAG pipeline using Streamlit
- Real-world application of RAG concepts to solve practical problems
Learning Roadmap
2.
The Basics of RAG
The Basics of RAG
Learn the logic behind RAG, its essential components, and strategies like indexing and retrieval to build a solid foundation for your RAG systems.
3.
RAGs and LangChain
RAGs and LangChain
4 Lessons
4 Lessons
Explore implementing indexing, querying, and response generation in LangChain to power your RAG systems.
4.
Build a Frontend for Our RAG System
Build a Frontend for Our RAG System
4 Lessons
4 Lessons
Use Streamlit and LangChain to build a user-friendly frontend for your RAG system, enabling seamless interaction with your pipeline.
5.
Challenges
Challenges
6 Lessons
6 Lessons
Tackle advanced challenges to enhance your system, like handling vector store transitions and supporting multiple file formats.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
Retrieval-augmented generation (RAG) is rapidly becoming the standard for building reliable, production-ready LLM applications. As generative models face limitations around hallucination and stale knowledge, RAG provides a structured way to ground outputs in real data, making it essential for any system that requires accuracy, context, and trust.
I built this course from my work in intelligent systems and adaptive AI, where combining retrieval with generation is critical for building systems that reason over dynamic information. A recurring pattern I observed was that developers could build LLM demos, but struggled to make them dependable in real-world scenarios. The missing piece was almost always retrieval. This course is designed to make RAG practical and approachable.
You’ll learn RAG fundamentals through its architecture and workflows, then implement end-to-end pipelines using LangChain. You’ll build a working RAG application and extend it with a Streamlit frontend, focusing on how to structure data, queries, and responses effectively.
Developers are already using RAG to power search, assistants, and enterprise AI systems. If you want to build LLM applications that are accurate and production-ready, this is where you start.
ABOUT THE AUTHOR
Khayyam Hashmi
Computer scientist and Generative AI and Machine Learning specialist. VP of Technical Content @ educative.io.
Trusted by 2.9 million developers working at companies
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Anthony Walker
@_webarchitect_
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Evan Dunbar
ML Engineer
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Software Developer
Carlos Matias La Borde
S
Souvik Kundu
Front-end Developer
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Vinay Krishnaiah
Software Developer
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