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!
Despite being well structured course material and passing relevant experinece, the code showcased, the libraries used are outdated.
Good but the lab was out of date, the dependencies and LLM calling didnt work
The course is awesome!. I got clear understanding of RAG and LlamaIndex
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.
This is an excellent course in which I learned about RAG.
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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 Overall, the course is outdated, and the labs are not functioning.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.
Good but the lab was out of date, the dependencies and LLM calling didnt work
The course is awesome!. I got clear understanding of RAG and LlamaIndex
This is an excellent course in which I learned about RAG.
Great learning materials and hands-on experience
excellent content and hands on excercise
Me ha abierto la mente
good explanation
finito il corso
Nice Course
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.
Good overall but; - Labs are extremely wordy and repetitive. - There is no actual coding required since they literally give you the solutions - Lab estimated time and actual required time vastly differs
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.