The concepts presented in this course were insightful and valuable, and I also appreciated the way the modules were structured. However, the overall teaching approach felt somewhat limited — it mainly involved clicking through steps and filling in pre-prepared code snippets. There were few opportunities for open-ended problem-solving or creating original code solutions. In many cases, simply copy-pasting from earlier cells or making minor edits to previous prompts was sufficient, which reduced the challenge and depth of the exercises.

This course gave me a solid understanding of Retrieval Augmented Generation, combining theory with hands-on projects using real-world tools like Weaviate and Phoenix. I now feel confident building scalable RAG systems and optimizing them for accuracy, latency, and cost."

The course is highly interesting and very useful. The explanations are clear, and the exercises are highly instructive. For Coursera: For those who rely on subtitles, it would be preferable if the transcript were displayed in the sidebar and scrolled automatically as the video progresses. In order for the certificate of completion to be used as valid proof of corporate training, it would be helpful if it included the completion date, total duration (in hours), grade, and information about the presence of intermediate or final assessments

It's not going to be much help if it's your introduction to building things, but if you can already build out an application and you're looking to understand concepts specific to RAG and working with LLMs this course is great. I built out a RAG app over the course of the lectures and I didn't really need anything beyond them for the RAG specific elements.

This course has been great to refresh my knowledge and deepen it. The course really helped to focus on the crucial building blocks for RAG systems and made it as easy as possible to follow along and learn.

I learnt quite a bit about LLMs, vector databases, RAG and various terms associated with this space. I came out better informed and hopefully learn more and implement these things in my projects

Excelente curso! El material abarca más de lo fundamental del tema y te va guiando por los conceptos de una manera fluida.

Fabulous explanation of basic to advanced RAG concept. A mandatory course for all the AI geeks out there.

The content is excellent, and Zain explains everything with calm clarity and a well-structured approach.

Very good and enjoyed the material. The instructor really understands the field and explains very well.

Great step-by-step introduction on RAG systems and get deeper understanding of its components.

Excellent course! Great detail and very well explained.

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,

Paid for the course, could not finish in time due to hectic work schedule, now i got some time to finish it, and it's asking to pay again.. It's not fair...

Having been architecting and building RAG-based application solutions for the past year and a half, I was pleasantly surprised to discover several valuable insights that I hadn't yet integrated into my own solutions. 💡 This course serves as an excellent foundation, guiding you through building a RAG application from the ground up. It covers essential concepts including: 1. Retrievers 🔍 2. Rerankers 📊 3. LLM considerations 🤖 4. And most importantly - Observability platforms 📈 Observability is one of the most under-discussed aspects in the world of LLM-powered agentic and RAG applications. I was especially excited to see all the relevant metrics clearly presented and seamlessly integrated into a unified observability framework. I will highly recommend this course to novice and practitioners alike as I am sure everyone will learn something new. I would like to express my gratitude to Zain Hasan and DeepLearning.AI for such a wonderful course. For anyone working with RAG systems, this course offers practical insights that can elevate your implementations! 🚀

Zain Hasan is one of the best instructors I have encountered at Coursera. To begin with, he speaks clearly, use standard English and precise wording, so I don't have to struggle to understand. He explains not only what to do but also why it works. The most important is the course design - every topic is built upon the previous ones. By the end of the course, I not only learned RAG but also gained general knowledge and conceptual understanding of keyword search, semantic search, embeddings, vector database and LLM. Great course. Thank you, Zain.

While I already had industry experience building advanced RAG systems, this course provided solid foundations, an excellent review of the topic, and exposed me to new algorithms, techniques and tools that I was unfamiliar with. I had learned RAG in an ad hoc manner and this course provided a structured and copmprehensive treatment of the topic. Finally, the instructor was simply excellent.

Great content and exercises. One important point not mentioned is that Oracle Database can also be used as a Vector Database option for RAG, and that Oracle Cloud (OCI) offers GPU Dedicated Clusters, which are excellent for AI/ML workloads.

An outstanding course! It makes the complex concepts of RAG incredibly clear and practical. The balance of theory and hands-on application is perfect. I highly recommend it to anyone serious about building next-gen AI. 👍

Clear, concise explanations that break down complex RAG concepts and mathematics into simple ideas. One of the best courses I’ve taken for gaining a solid understanding of building enterprise-grade RAG solutions.