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Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) improves large language model (LLM) responses by retrieving relevant data from knowledge bases—often private, recent, or domain-specific—and using it to generate more accurate, grounded answers. In this course, you’ll learn how to build RAG systems that connect LLMs to external data sources. You’ll explore core components like retrievers, vector databases, and language models, and apply key techniques at both the component and system level. Through hands-on work with real production tools, you’ll gain the skills to design, refine, and evaluate reliable RAG pipelines—and adapt to new methods as the field advances. Across five modules, you'll complete hands-on programming assignments that guide you through building each core part of a RAG system, from simple prototypes to production-ready components. Through hands-on labs, you’ll: - Build your first RAG system by writing retrieval and prompt augmentation functions and passing structured input into an LLM. - Implement and compare retrieval methods like semantic search, BM25, and Reciprocal Rank Fusion to see how each impacts LLM responses. - Scale your RAG system using Weaviate and a real news dataset—chunking, indexing, and retrieving documents with a vector database. - Develop a domain-specific chatbot for a fictional clothing store that answers FAQs and provides product suggestions based on a custom dataset. - Improve chatbot reliability by handling real-world challenges like dynamic pricing and logging user interactions for monitoring and debugging. - Develop a domain-specific chatbot using open-source LLMs hosted by Together AI for a fictional clothing store that answers FAQs and provides product suggestions based on a custom dataset. You’ll apply your skills using real-world data from domains like media, healthcare, and e-commerce. By the end of the course, you’ll combine everything you’ve learned to implement a fully functional, more advanced RAG system tailored to your project’s needs.

状态:Vector Databases
状态:Embeddings
中级课程小时

精选评论

SR

5.0评论日期:Aug 4, 2025

Amazing course on RAG systems at production scale.

SK

4.0评论日期:Aug 31, 2025

explains the key concepts very well. code examples are also good to build on the concepts

RS

5.0评论日期:Aug 13, 2025

I learnt quite a bit about LLMs, vector databases, RAG and various terms associated with this space. I came out better informed and hopefully learn more and implement these things in my projects

SB

4.0评论日期:Nov 12, 2025

Really interesting ! However the notebooks contained a lot of verbose code , in which we only change few lines of code. Appart from that , perfect !

BC

5.0评论日期:Aug 1, 2025

Great step-by-step introduction on RAG systems and get deeper understanding of its components.

MF

5.0评论日期:Jul 27, 2025

Excellent course! Great detail and very well explained.

CL

5.0评论日期:Dec 15, 2025

I found this course very useful, particularly good at covering the fundamental aspects of LLMs and RAG.

MB

5.0评论日期:Aug 31, 2025

Excellent course, with detailed explanation of topics with practical guidance

MM

5.0评论日期:Jan 27, 2026

Very good introduction to the concepts and principals of RAG, with notebooks to demonstrate the concepts.

AZ

5.0评论日期:Nov 8, 2025

Excellent course. It covers every important detail of RAG systems with clarity, the instructor is amazing, and it provides a solid foundation for anyone looking to understand or build RAG pipelines

RP

4.0评论日期:Jan 7, 2026

The course is great but the exercises and assignments could be more challenging.

AD

5.0评论日期:Aug 12, 2025

Fabulous explanation of basic to advanced RAG concept. A mandatory course for all the AI geeks out there.

所有审阅

显示:20/51

Robin Fuchs
4.0
评论日期:Aug 21, 2025
Seelam Srinivasa Reddy
5.0
评论日期:Jul 28, 2025
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5.0
评论日期:Sep 15, 2025
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5.0
评论日期:Jul 22, 2025
Ravi Sachidanandam
5.0
评论日期:Aug 13, 2025
Raúl Alvarado
5.0
评论日期:Aug 11, 2025
Aritra Dutta
5.0
评论日期:Aug 13, 2025
Prakash joshi
5.0
评论日期:Aug 15, 2025
Juan José Expósito González
5.0
评论日期:Jul 27, 2025
Ben De Corte
5.0
评论日期:Aug 2, 2025
Michael Fien
5.0
评论日期:Jul 28, 2025
Dipanjan Ghosal
5.0
评论日期:Sep 4, 2025
Pierre de Lacaze
5.0
评论日期:Sep 8, 2025
Paulo Portugal
5.0
评论日期:Sep 5, 2025
Karsten Zenk
5.0
评论日期:Sep 16, 2025
Ambrish Kinariwala
5.0
评论日期:Mar 13, 2026
Bart W Jenkins
5.0
评论日期:Sep 9, 2025
Yuriy
5.0
评论日期:Sep 26, 2025
Abdelrahman Zeidan
5.0
评论日期:Nov 8, 2025
Vishal Jain
5.0
评论日期:Aug 28, 2025