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Learner Reviews & Feedback for Retrieval Augmented Generation (RAG) by DeepLearning.AI
88 ratings
About the Course
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.
Top reviews
RS
Aug 14, 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
VJ
Aug 28, 2025
I believe the course covers alot of ground and provides good depth for each component. This will definitely provide solid foundation to anyone who was worked with LLMs in the past
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26 - 26 of 26 Reviews for Retrieval Augmented Generation (RAG)
By Alon T
•Aug 10, 2025
Overly technical, lacks conceptual grounding in generative models.