Since this video turned out to be so successful and several people requested for me to do a deep dive / demo, here it is! Looking forward to reading your comments and hope you enjoy this one too. ruclips.net/video/P8tOjiYEFqU/видео.html
I agree. Most other explanations are either way too detailed with live coding that muddles the information or way too high-level talking about how the LLM retrieves the additional data (which it doesn't! it is given to it via the prompt!)
This is one of the best explanations of large language Models and the value of utilizing RAG I have seen. Don, you are an outstanding communicator. Thank you for taking the time to put this together.
This is much better than the IBM video. They make the assumption that the LLM is transparent and trained by the developer and that the prompt is a mix of word prompt and numerical vectors that are from a different embedding, which just seems wrong. This makes a lot more sense. Prompt -> Vector Database + Nearest Neighbors -> Top k -> Paragraphs ->
Thanks Prof. Don Woodlock you have explained exactly the same as I need to understand about my current project every concept maps to the practical part of project. Please deliver your knowledge more about advance and complex topics.👍
I was looking for a general explanation to the RAG topic and you provide it very well! Now, I understand that the quality of RAG systems strongly depend on the information retrieval from the vector database. I will try to implement a RAG system on my own to learn something about it. Thank you very much!
Great explanation, I have seen a lot of these and people normally go into far too much detail and muddy the water, or are far too abstract, fast and loose, or just get it wrong. I think this is a great level to cover this topic at.
Great video. I feel like this is the first time I'm learning stuff that is at the cutting edge. This video was posted 2 months ago, very exciting times
Thanks a lot for this video. It helped me to understand how the question is treated for the vector db, so the whole pre-prompt part. Now finally RAG makes sense to me :) ThumbsUP!
great session dear Don. It was very complete, to the point and simply more advanced than other popular videos but of course in simple words. Thank you so much sir. ❤❤
Thank you very much for this great presentation. Tomorrow I will use this video to describe in a few minutes to the decision-makers in my company what this RAG means. Many simply do not yet understand the simplicity behind the pattern and therefore the possibilities I find it difficult to present it in such simplicity Thank you very much🙂
Doing RAG stuff right now for work. Just scratching the surface, but very interesting stuff so far. We have a few clients on the horizon who really just need text classification, and the vanilla results from the vector DB might actually be good enough for them. Interesting territory coming fast.
Sounds like it is optimizing or creating a more efficient prompt session? I guess "augmentation" is a fairly good description. Thank you. I enjoy your teaching style.
This will simplify the "RAG" stuff -> take a general language model like GPT, Gemini, Llama, and tailor it to answer only things you care about -> sports scores, latest movies, celebrity gossip etc. -> by feeding the model your own data. Data can be real time like from a news website that offers an API or some textbook that is in PDF format.
You did well, however your explanation still falls under basic prompting. Yea there is some sort of retrieval in your explanation but it becomes RAG when their is some sort of retrieval that is generative and that is where the concept of vector database comes in.
Cautionary note on loading up your prompts: transformer-based LLMs are quadratic wrt infrastructure. Assuming you have a decent size context window, doubling your prompt size requires 4X as much infrastructure (GPUs, in particular). Leaning into complex prompts is very much a model-maker approaches this issue - but, it can be very impractical for those of us that actually build high ROI, production-grade GenAI workloads. Models are only a backend tool. So, while Anthropic, Google, etc. promote a future with million token context windows, this can be impractical for most 'real' prod workloads.
![Jack Harlow - Hello Miss Johnson [Official Music Video]](http://i.ytimg.com/vi/Q86_nlRoIGw/mqdefault.jpg)
Since this video turned out to be so successful and several people requested for me to do a deep dive / demo, here it is! Looking forward to reading your comments and hope you enjoy this one too. ruclips.net/video/P8tOjiYEFqU/видео.html
One of the best explanation of RAG on RUclips. Thanks Don.
I agree. Most other explanations are either way too detailed with live coding that muddles the information or way too high-level talking about how the LLM retrieves the additional data (which it doesn't! it is given to it via the prompt!)
This is one of the best explanations of large language Models and the value of utilizing RAG I have seen. Don, you are an outstanding communicator. Thank you for taking the time to put this together.
Finally someone is explaining with an real time example. Otherwise everyone else takes an example of fruits (apple, oranges etc) or movie names etc.
Good point!
Very good point
Not confusing at all, just simple and get to the point explanation, thank you.
10:18 wasnt confusing at all, your diagram was very helpful sir
This is much better than the IBM video. They make the assumption that the LLM is transparent and trained by the developer and that the prompt is a mix of word prompt and numerical vectors that are from a different embedding, which just seems wrong. This makes a lot more sense. Prompt -> Vector Database + Nearest Neighbors -> Top k -> Paragraphs ->
I found your video to be the most accessible and informative introduction to RAG, especially for those new to this topic.
You explained it very well even for an audience already from ML/NLP background.
one of the best explanation i ever found. Now I finally understand what RAG is and thank you so much Mr. Don
This is probably the best tutorial I have watched. Period. What an amazing teacher!
Thank you. You are the first to explain RAG well. I have hear about a lot without understanding what does it mean.
Thanks Prof. Don Woodlock you have explained exactly the same as I need to understand about my current project every concept maps to the practical part of project. Please deliver your knowledge more about advance and complex topics.👍
Fantastic Simple and lucid. You are a wonderful teacher
Probably the best ever explanation on RAG
I was looking for a general explanation to the RAG topic and you provide it very well! Now, I understand that the quality of RAG systems strongly depend on the information retrieval from the vector database. I will try to implement a RAG system on my own to learn something about it. Thank you very much!
WOW! The simplest yet the best explanation! It's easy to understand for a beginner like me.
THANK YOU!
By far this is the most simple explaination for RAG I have came across. Amazing.
Looking forward to next videos in series.
Great explanation. I believe this has a big market for developers in small towns. Such an easy product to create and sell.
Such a great explanation of RAG. It really helped me grasp the power of it.
Thanks for such a simple explanation of the RAG Architecture Concepts.
Wow he has explained this really clearly. This is the missing link for me between LLMs and making them actually useful for my projects. Thank you!
This great explanation on RAG enlightened me, thank you so much for this. It is very educational, warm and nicely delivered.
Wow.. Job well done. Great and simplistic explanation for such complex topic.
The best and most complete explanation I found on RUclips❤
Wow! Wonderful session. I loved the simplicity of explaining RAG. Thanks a lot Don.
I finally understood what RAG is, including the vector part. Great!
Great explanation, I have seen a lot of these and people normally go into far too much detail and muddy the water, or are far too abstract, fast and loose, or just get it wrong. I think this is a great level to cover this topic at.
You are Simple, Succinct and absolutely effective. I have understood RAG much better now. Thanks a ton !
Best content on RAG!! Thank you!
the best explanation of rag that've found thank you a lot
Great video. I feel like this is the first time I'm learning stuff that is at the cutting edge. This video was posted 2 months ago, very exciting times
Same here I had no idea that RAG WAS BIG DEAL. I'VE BEEN READING STUFF ON REDDIT WORK PEOPLE TALKING ABOUT THE RAG THIS AND THAT
This explanation is absolutely S-Tier... Round of applause for this brother! What a great weaving of concepts.
This has been the most helpful video I've found to help me understand how RAG works. Thank you so much for your wonderful explanation!
What a fantastic way to explain a rather complex topic, the multiple complex components in play without miring us in the tech minutia. Refreshing!
Thank you very much for this video. Now is understand what my colleagues do in work with system documentation handling with use of LLM.:)
This is really clear, this will customize the output based on the environment of the user not just on open source data.
Very clear explanation for the RAG in real world application!
Thanks a lot for this video. It helped me to understand how the question is treated for the vector db, so the whole pre-prompt part. Now finally RAG makes sense to me :) ThumbsUP!
Thank you Don , wonderful explanation..
Hello Don Sir, thanks for this explanation. You're a blessed master craftsman. Simple and precise description and to the point.
Excellent explanation Mr. Author
This is an excellent explanation of the concept. Thank you Don
9:20 not at all confusing, makes perfecf sense the way u exolained it thank you!!!
great session dear Don. It was very complete, to the point and simply more advanced than other popular videos but of course in simple words. Thank you so much sir. ❤❤
Thank you for sharing your knowledge with us, great explanation.
Greatly explained. Quality content. Thank you very much!
This was great, thank you! I believe this process is what Copilot for Microsoft 365 uses and it is referred to as ‘grounding’. Very helpful 👍
wow! 10:10 Nobody explained this in my past 5 videos about RAG! Thanks Don ❤
I've been doing RAG and not even knowing the definition. Was glad to see I wasn't doing it wrong by injecting it into the end of the prompt.
Thank you very much for this great presentation.
Tomorrow I will use this video to describe in a few minutes to the decision-makers in my company what this RAG means.
Many simply do not yet understand the simplicity behind the pattern and therefore the possibilities
I find it difficult to present it in such simplicity
Thank you very much🙂
what a great explanation of RAG! Thank you
that was the best explanation I have seen so far! Thank you very much!
Brilliant video, consise and clear. Many thanks.
Best RAG explanation I think. Thanks.
Extremely good and simple to understand. This is my first comment to share from ages
Excellent explanation. Exactly what I was looking for! Thank you, Don!
Awesome, thanks for the wonderful explanation in simple language
So simple explanation 🤓.Thank you.
I absolutely loved this explanation, this was so intuitive to understand
Greatly appreciated for this wonderfully explained video
Great work; would really love to see you dig in on tokens and how they work as well.
Bestt explanation! Thank youu Mr.Don!
Best explanation on RAG!
Wonderful explanation of this topic. Thank you!
The best explanation ever fro RAG
Thank you Don! The explanation was delightful
Doing RAG stuff right now for work. Just scratching the surface, but very interesting stuff so far. We have a few clients on the horizon who really just need text classification, and the vanilla results from the vector DB might actually be good enough for them. Interesting territory coming fast.
yes - I have found that pretty small LLMs (like BERT) do just fine for text classification.
Great explanation of RAG. I subscribed to your channel after watching this. Thank you Don for the great content.
This was well explained, including the messy diagram .. lol .. thanks for that background!
Amazing video. Thanks for the great explanation!
Very Good Explanation Sir
Good content, please share more.
Thank you so much. You make it very easy to understand! 😊
Just I watched from youtube suggestions and you me good explanations on Retrieval augmented generation closure to my use case. Thank you
Awesome video! It helped explain the concept a great deal. well done👍
This is one of the best RAG explanations I’ve seen so far!
Wss it that hard?
Well done 🎉
thank you sir for clearing concepts!
Appreciate you and your content. I'm glad I found you again
Very good introduction!!!
One of the best way of explanation.. thanks for doing this.
Concise and simplified !! Thank you, Don !
Sounds like it is optimizing or creating a more efficient prompt session? I guess "augmentation" is a fairly good description.
Thank you. I enjoy your teaching style.
This was so clear. Thank you!
such a good explanation, thank you so much!!
Great explanation, thanks, Don :)
This will simplify the "RAG" stuff -> take a general language model like GPT, Gemini, Llama, and tailor it to answer only things you care about -> sports scores, latest movies, celebrity gossip etc. -> by feeding the model your own data. Data can be real time like from a news website that offers an API or some textbook that is in PDF format.
You did well, however your explanation still falls under basic prompting. Yea there is some sort of retrieval in your explanation but it becomes RAG when their is some sort of retrieval that is generative and that is where the concept of vector database comes in.
Thanks for an excellent presentation.
What tools are you using for drawing .. that was an impressive way to present
Great video. Appreciate your work!
it’s a pretty good explanation,thanks Don
Thank you, Don! Could you explain some difference between RAG (Vector DB) and Knowledge graph usage with LLM.
Thank you for your great explanation sir!
Thank you so much! This is a great, easy-to-follow explanation. Coincidentally, I'm having knee surgery tomorrow. LOL.
Did you prepare properly??
thanks. learned about RAG, and liked your style
I was expecting something practical. But this is really helpful
Cautionary note on loading up your prompts: transformer-based LLMs are quadratic wrt infrastructure. Assuming you have a decent size context window, doubling your prompt size requires 4X as much infrastructure (GPUs, in particular). Leaning into complex prompts is very much a model-maker approaches this issue - but, it can be very impractical for those of us that actually build high ROI, production-grade GenAI workloads. Models are only a backend tool. So, while Anthropic, Google, etc. promote a future with million token context windows, this can be impractical for most 'real' prod workloads.
loved your explanation, thank you
Best explanation ever.
stellar explanation!!
Thanks for this great explanation, Don!