Well, the labs are faulty and frustrating. I would prefer running them in a local Jupyter notebook. Overall, the course is outdated, and the labs are not functioning. The course material is so short and the provided instructions are not enough compared to the level of the course. I will apply Courseara to get my money back.

The course enabled me to deepen my knowledge and understanding about RAG. I gained new skills.

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

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

excellent content and hands on excercise

good explanation

finito il corso

nice

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.

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.

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being well structured course material and passing relevant experinece, the code showcased, the libraries used are outdated.

The comparison of LangChain and LlamaIndex brings clarity.

Nice course, llamaindex can be better

The first module of this course was great. The second was confusing; why are we learning about Gradio? In the third, I understand the focus on LlamaIndex, but would have liked more of a conceptual understanding of how to apply RAG before launching into an argument for LlamaIndex. That said, it was thorough. For some reason, the practice chatbot was asking more conceptual questions about RAG that were not covered in the course, but I wish they had been! The quizzes were all about coding and, again, Gradio. Like, why? The lab in the second module was not really helpful; it was basically filling in parameters / variables in code, but who remembers code like that? And the links back to resources just took you to the home page (of the course, I think). So that wasn't helpful. Ultimately, I'm mostly frustrated by the Gradio thing. We just went over Flask in the previous course in this sequence; why do we need Gradio too? I used to teach data science full-time and know how to contextualize all of this, but someone else might not.

There are some issues with IBM WatsonX libraries, which blocked me in completing some of the hands-on lab practice

Basically a LlamaIndex / LangChain / Gradio tutorial

Below average course. Concepts are not defined, tests are before material, practice lab requires knowledge from other courses (need to find where in other courses), low quality of videos and materials. It seems the entire program around IBM RAG and Agentic AI is poorly designed. I am starting to think that it is best to avoid IBM courses on AI, as they are not the leaders in that field.

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