Name: Retrieval Augmented Generation (RAG)
Brand: DeepLearning.AI
Availability: OnlineOnly
Rating: 4.8 (50 reviews)

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学生对 DeepLearning.AI 提供的 Retrieval Augmented Generation (RAG) 的评价和反馈

4.8

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186 个评分

课程概述

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....

26 - Retrieval Augmented Generation (RAG) 的 50 个评论（共 50 个）

创建者 David K

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Dec 12, 2025

Great course! However it will not do professional from you ;) Learn, learn, learn

创建者 Devi n s (

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Nov 5, 2025

This was really helpful in understanding the concepts and applications of RAG

创建者 Madhur B

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Sep 1, 2025

Excellent course, with detailed explanation of topics with practical guidance

创建者 Gonzalo J T D

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Oct 7, 2025

Great course! All the information you need for later, go deep and practice.

创建者 Juan C Z

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Aug 31, 2025

Increíble curso, detallado, intenso y didáctico. Muy recomendado

创建者 Sebastian R

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Aug 5, 2025

Amazing course on RAG systems at production scale.

创建者 Austin H

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Nov 7, 2025

Very well designed course, well worth the time.

创建者 Michel W

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Sep 24, 2025

Excellent approach for RAG including LLM use

创建者 Heng-Yi C

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Nov 14, 2025

An excellent course for LLM RAG begineer.

创建者 tumpa b

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Jan 25, 2026

A useful course for AI/LLM professionals

创建者 Gonzalo Á F

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Oct 4, 2025

I learned a lot about real RAG systems

创建者 Vicente V J

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Nov 13, 2025

Comprehensive course by deeplearning

创建者 Jacky W

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Nov 10, 2025

The Best RAG Tutorial in 2025!!!

创建者 Rajesh I

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Aug 25, 2025

Gave good understanding on RAG.

创建者 Omar

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Aug 29, 2025

What a GRATE course to have !!

创建者 Yue.Zhao

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Oct 25, 2025

very useful, Thank you!

创建者 aqadadeh

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Oct 5, 2025

Very Good

创建者 Santiago S

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Oct 3, 2025

The instructor and lessons were excellent. It is also a highly relevant course for today's software engineers. However, I think more attention should be put to the Jupyter Notebook tasks. One had a bug that prevented correct grading (which affected my course final grade), and another had a bug that made impossible to do one part of the assignment. One even had factually incorrect information. Even with this challenges, I am happy I took this course. I did learn a lot and it was a very rewarding and productive use of my time.

创建者 Sami B

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Nov 13, 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 !

创建者 Siddartha K

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Sep 1, 2025

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

创建者 Raphael O P

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Jan 8, 2026

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

创建者 CHUNG-HAO ( F

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Feb 3, 2026

Good

创建者 Vicente F M

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Oct 23, 2025

I’m really enjoying the course so far, but I think it would greatly benefit from a section explaining how to set up a local environment on Windows to run the Python notebooks. Many of us following the course would appreciate clear instructions for configuring a local setup to practice and experiment more effectively. Thank you for considering this suggestion! Best regards,