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!
AB
This is an excellent course in which I learned about RAG.
RR
Despite being well structured course material and passing relevant experinece, the code showcased, the libraries used are outdated.
MM
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.
VN
The course is awesome!. I got clear understanding of RAG and LlamaIndex
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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 Overall, the course is outdated, and the labs are not functioning.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
Me ha abierto la mente
good explanation
finito il corso
good
GOOD
nice
ok
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.
Good course with practical examples. Time estimates for labs are seriously underestimated. Lab in module 2 (qa) refers to another course to obtain information needed to complete the task. This increases the time needed to complete the tab even more. That lab does not provide a solution to the task which is bad because the solution is not trivial. Fortunately the discussion forum moderators are respond quickly and are very helpful.
There are some issues with IBM WatsonX libraries, which blocked me in completing some of the hands-on lab practice