Coursera – Retrieval Augmented Generation (RAG) 2025-7

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. 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.

What you’ll learn

• How to design and build RAG systems tailored to real-world needs

• How to weigh tradeoffs between cost, speed, and quality to choose the right techniques for each component of a RAG system

• A foundational framework to adapt RAG systems as new tools and methods emerge

Specificatoin of Retrieval Augmented Generation (RAG)

• Publisher : Coursera
• Teacher : Zain Hasan
• Language : English
• Level : Intermediate
• Number of Course : 5
• Duration : 3 weeks to complete at 10 hours a week

Content of Retrieval Augmented Generation (RAG)

Requirements

• Intermediate Python skills required; basic knowledge of generative AI and high school–level math is helpful

Pictures

Sample Clip

Installation Guide

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Subtitle : English

Quality: 720p

The new version has added assignments along with code compared to the previous version.

Download Links

Download Part 1 – 1 GB

Download Part 2 – 227 MB

File size

1.19 GB