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返回到 Build RAG Applications: Get Started

学生对 IBM 提供的 Build RAG Applications: Get Started 的评价和反馈

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57 个评分

课程概述

Data Scientists, AI Researchers, Robotics Engineers, and others who can use Retrieval-Augmented Generation (RAG) can expect to earn entry-level salaries ranging from USD 93,386 to USD 110,720 annually, with highly experienced AI engineers earning as much as USD 172,468 annually (Source: ZipRecruiter). In this beginner-friendly short course, you’ll begin by exploring RAG fundamentals—learning how RAG enhances information retrieval and user interactions—before building your first RAG pipeline. Next, you’ll discover how to create user-friendly Generative AI applications using Python and Gradio, gaining experience with moving from project planning to constructing a QA bot that can answer questions using information contained in source documents. Finally, you’ll learn about LlamaIndex, a popular framework for building RAG applications. Moreover, you’ll compare LlamaIndex with LangChain and develop a RAG application using LlamaIndex. Throughout this course, you’ll engage in interactive hands-on labs and leverage multiple LLMs, gaining the skills needed to design, implement, and deploy AI-driven solutions that deliver meaningful, context-aware user experiences. Enroll now to gain valuable RAG skills!...

热门审阅

VN

Aug 30, 2025

The course is awesome!. I got clear understanding of RAG and LlamaIndex

AB

Jul 22, 2025

This is an excellent course in which I learned about RAG.

筛选依据：

1 - Build RAG Applications: Get Started 的 10 个评论（共 10 个）

创建者 veluri n

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Aug 31, 2025

The course is awesome!. I got clear understanding of RAG and LlamaIndex

创建者 Ana A B

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Jul 23, 2025

This is an excellent course in which I learned about RAG.

创建者 prateek k g

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Sep 3, 2025

excellent content and hands on excercise

创建者 Emanuele P

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Jul 23, 2025

finito il corso

创建者 Shenyi B

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Sep 28, 2025

Rest is very good. So far there is 2 issues that I faced 1. The browswer lab took way longer time, also could be interrupted without reasons. My internet is 1 GB fiber, no issue in past. prefer to have local repo to do practice. 2. LlamaIndex was jump in right away after langchain, which makes learner very confused, may tell reader it is good for buidking quick demo, but less flexiablie than langchain.

创建者 Miguel M

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Sep 21, 2025

Hola, el curso es muy bueno y los contenidos muy valiosos. Los laboratorios, personalmente los prefiero en Jupyter, pero parte del aprendizaje es ser flexible. Muchisimas gracias, Miguel.

创建者 RANJEET K R

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Aug 24, 2025

Despite being well structured course material and passing relevant experinece, the code showcased, the libraries used are outdated.

创建者 Laxman G

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Apr 28, 2025

The comparison of LangChain and LlamaIndex brings clarity.

创建者 Shakti P M

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Sep 29, 2025

Nice course, llamaindex can be better

创建者 OM S

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Sep 21, 2025

WORST TEACHINGS HERE, NOT PROPERLY TAUGHT. EXPECTED MUCH BETTER .... I WANTED TO DOWNLOAD ALL THE PDF AND PPT SLIDES SHOWN IN THE VIDEOS. FOR SOME REASON I DONT FIND ANY ??? REALLY WORST TEACHERS HERE , THEY DONT PROVIDE PDF FILES FOR THE PRESENTATION. I WILL NOT RECOMMEND THIS COURSE TO ANYONE