LangChain 101 for Beginners (OpenAI / ChatGPT / LLMOps) (Udemy.com)

This is a good course, very entry level. Definitely a great basic intro to LangChain. I found myself saying "Why?" a lot, so I guess one small suggestion for improvement would be to provide a little more context to things. Like, for instance, why do we load the TikToken library?

What I liked the most about this course is it was compact and to the point. In this fast running world, most of the times we do not have time for doing long courses and we end up losing interest in such courses but due to the abstract nature of this course, it kept me interested till the end of the course.

great course but so many of the langchain modules have deprecated or relocated in only a year, so pretty much every example had to be heavily rewritten. The principles were all there and the instructor was very good.

LangChain is a library that is constantly updated. For this reason to use some of the examples I had to lookup the new imports and do minor changes to the source code. It would be great if you could pin the version of pip libraries you are using to ensure that we get the same results.

I thoroughly enjoyed this course. Unlike lengthy 10-hour or 24-hour courses, it provides a concise yet comprehensive introduction. It serves as an excellent starting point, allowing us to delve deeper into the subject matter. A solid 5 out of 5! I hope more people create similar focused courses.

This course is excellent to give you a good idea about how you can write code to use LLMs with LangChain, within an hour. I haven't found any other course that covers this level of detail within an hour. I've docked half a star because langchain evolves really fast, and some of the code used in the lecture may already be obsolete. For example, instead of using chains like RetrievalQA, the preferred approach today is to use LCEL. This gap could be fixed in two ways: 1. Constantly keep updating the course as langchain evolves - the preferred approach to keep the course relevant for longer. 2. Mention a specific version of langchain and associated modules to be downloaded, so that the code works. This would be ok to expain concepts, though the student would then need to go out and learn the up-to-date syntax.

I don't feel like I learned how to use langChain after this course, it feels more of a general showcase of its features. It's goes through very specific examples, some of it feels a bit rushed, student questions are left unanswered and it ends with 'see you in the next lecture' when there aren't any other lectures available. However, the explanations are clear, well spoken and it does cover the basics of langChain, I just wish it went into more detail.

This course by Avinash exceeded expectations with its practical approach and to the point content. Avinash's expertise as an instructor shone through in his clear teaching style and dedication to real-world applications. The course strikes an excellent balance between theory and practice, providing valuable skills for harnessing language models in Python applications.

This was packed full of useful examples that can be applied during the course or soon after. The only think missing are pointers to deeper dives and if/how we can the same with other llms besides OpenAI. Lesson 4 was a great tease for what might be possible with langchain outside of openai, but that info was only in one lesson.

A great course that is the perfect length each lesson and straight to the point. Would be great to see some different methods of each topic, such as, using Pinecone for online storage or other ways to store conversation history for chat bots in production. Avi is a great presenter/speaker and it's easy to stay focused with him even during fast paced delivery.