1. Understanding the challenges with LLMs - 0:36 2. Introducing Retrieval-Augmented Generation (RAG) to solve LLM issues - 0:18 3. Using RAG to provide accurate, up-to-date information - 1:26 4. Demonstrating how RAG uses a content store to improve responses - 3:02 5. Explaining the three-part prompt in the RAG framework - 4:13 6. Addressing how RAG keeps LLMs current without retraining - 4:38 7. Highlighting the use of primary sources to prevent data hallucination - 5:02 8. Discussing the importance of improving both the retriever and the generative model - 6:01
Very well explained!!! Thank you for your explanation of this. I’m so tired of 45 minute UA-cam videos with a college educated professional trying to explain ML topics. If you can’t explain a topic in your own language in 10 minutes or less than you have failed to either understand it yourself or communicate effectively.
Einstein said, "If you can't explain it simply, you don't understand it well enough." And you explained it beautifuly in most simple and easy to understand way 👏👏. Thank you
I believe the video is slightly inaccurate. As one of the commenters mentioned, the LLM is frozen and the act of interfacing with external sources and vector datastores is not carried out by the LLM. The following is the actual flow: Step 1: User makes a prompt Step 2: Prompt is converted to a vector embedding Step 3: Nearby documents in vector space are selected Step 4: Prompt is sent along with selected documents as context Step 5: LLM responds with given context Please correct me if I'm wrong.
I’m not sure. Looking at OpenAI documentation on RAG, they have a similar flow as demonstrated in this video. I think the retrieval of external data is considered to be part of the LLM (at least per OpenAI)
I do not think retrieval is part of LLM. LLM is the best model at the end of convergence after training. It can't be modified rather after LLM response you can always use that info for next flow of retrieval
That's a really great explanation of RAG in terms most people will understand. I was also sufficiently fascinated by how the writing on glass was done to go hunt down the answer from other comments!
Just simply write on a glass board ,record it from the other side and laterally flip the image! Simple aa that.. and pls dont distract people from the contents being lectured by thinkin about the process behind the rec🤣
@@aykoch she is right handed. when she writes, the arm moves away from the body. left hand arm would move toward the body. because the video is flipped, it's a bit of a mind trick to see it.
@@jsonbourne8122 Nice attention to detail as they made sure the outfit was symmetrical without any logos and had a ring on each hand's ring finger, making it harder to tell it was flipped.
The explanation was spot on! IBM is the go to platform to learn about new technology with their high quality content explained and illustrated with so much simplicity.
Wow, I opened youtube coming from the ibm blog just to leave a comment. Clearly explained, very good example, and well presented as well!! :) Thank you
The interesting part is not retrieval from the internet, but retrieval from long term memory, and with a stated objective that builds on such long term memory, and continually gives it "maintenance" so it's efficient and effective to answer. LLMs are awesome because even though there are many challenges ahead, they sort of give us a hint of what's possible, without them it would be hard to have the motivation to follow the road
I spent all of the 1st watch talking while a friend watched it aswell trying to figure out is she is a robot because of the backwards writing. Good and fast info the 2nd watch. Great job
We also need the models to cross check their own answers with the sources of information before printing out the answer to the user. There is no self control today. Models just say things. "I don't know" is actually a perfectly fine answer sometimes!
I have few questions here @ (1) When I prompt and it is not present in context store, shall I get generated text from LLM? 2. when I prompt and a match with embeddings of context store, shall I get content generated from both LLM and Context store? 3. How to enforce RAG framework in Langchain? Appreciate answers
Nicely explained. My questions/doubts? 1. Doesn't this raise questions about the process of building and testing LLMs? 2. In such scenarios will the test and training data used be considered authentic and not "limited and biased"? 3. Is there a process/standard on how often the "primary source data" should be updated?
Thanks. Great video. I've had too many conversations where Chatgpt has apparently just made stuff up. I know that's not what happens really, but it seems like it and it still makes untrue statements. I'm glad researchers are working to improve things.
tokens as a [word] is what I'm working on right now (solo, self learning LLM techniques), this video helped me realize how the model doesn't know what it's outputting obviously, but AI-AI is different, so building tokens that have dimensional vectors that process in a separate model, can be used for explainable AI.
meaning a separate model processes the response itself, meta, it's for building evolution learning. AI-AI machine learning, you need a way to configure in between the iterations.
The video is short and consice yet the delivery is very elegant. She might be the best instructor that have teached me. Any idea how the video was created?
This lecturer should be given credit for such an amazing explanation.
I was thinking the same, she explained this so clearly.
Yes this was excellently explained, kudos to her.
Or at least credit for being able to write backwards!
The connection between a human answering a question in real life vs how LLMs (with or without RAG) do it was so helpful!
Why. Chat gpt wrote it
IBM should start a learning platform. Their videos are so good.
i think they already do
Yes, they have it already. UA-cam.
Its mirrored video, she wrote naturally and video was mirrored later
They have skill build but not videos at least most of the content
They do, I recently attended a week long AI workshop based on an IBM curriculum
I love seeing a large company like IBM invest in educating the public with free content! You all rock!
Apparently there are scientists in charge who are pushing for such an agenda. Love to see it.
4:15 Marina combines the colors of the word prompt to emphasis her point. Nice touch
Marina is a talented teacher. This was brief, clear and enjoyable.
1. Understanding the challenges with LLMs - 0:36
2. Introducing Retrieval-Augmented Generation (RAG) to solve LLM issues - 0:18
3. Using RAG to provide accurate, up-to-date information - 1:26
4. Demonstrating how RAG uses a content store to improve responses - 3:02
5. Explaining the three-part prompt in the RAG framework - 4:13
6. Addressing how RAG keeps LLMs current without retraining - 4:38
7. Highlighting the use of primary sources to prevent data hallucination - 5:02
8. Discussing the importance of improving both the retriever and the generative model - 6:01
Very well explained!!! Thank you for your explanation of this. I’m so tired of 45 minute UA-cam videos with a college educated professional trying to explain ML topics. If you can’t explain a topic in your own language in 10 minutes or less than you have failed to either understand it yourself or communicate effectively.
Wow, this is the best beginner's introduction I've seen on RAG!
I'm sure it was already said, but this video is the most thorough, simple way I've seen RAG explained on YT hands down. Well done.
Einstein said, "If you can't explain it simply, you don't understand it well enough." And you explained it beautifuly in most simple and easy to understand way 👏👏. Thank you
I love IBM teachers/trainers, I used to work at IBM and their in-house education quality was AMAZING!
I believe the video is slightly inaccurate. As one of the commenters mentioned, the LLM is frozen and the act of interfacing with external sources and vector datastores is not carried out by the LLM.
The following is the actual flow:
Step 1: User makes a prompt
Step 2: Prompt is converted to a vector embedding
Step 3: Nearby documents in vector space are selected
Step 4: Prompt is sent along with selected documents as context
Step 5: LLM responds with given context
Please correct me if I'm wrong.
I’m not sure. Looking at OpenAI documentation on RAG, they have a similar flow as demonstrated in this video. I think the retrieval of external data is considered to be part of the LLM (at least per OpenAI)
I do not think retrieval is part of LLM. LLM is the best model at the end of convergence after training. It can't be modified rather after LLM response you can always use that info for next flow of retrieval
Thank you. So many people praising this even though it didn't explain anything that can't be googled in 2 seconds.
That's a really great explanation of RAG in terms most people will understand. I was also sufficiently fascinated by how the writing on glass was done to go hunt down the answer from other comments!
hold up - the fact that the board is flipped is the most underrated modern education marvel nobody's talking about
I know, right?!
Probably they filmed it in front of a glass board and flipped the video on edition later on
Filmed in front of a non-reflective mirror.
Just simply write on a glass board ,record it from the other side and laterally flip the image! Simple aa that.. and pls dont distract people from the contents being lectured by thinkin about the process behind the rec🤣
Is the board fliped or has she been flipped?
Loved the simple example to describe how RAG can be used to augment the responses of LLM models.
Your ability to write backwards on the glass is amazing! ;-)
They flip the video
@@jsonbourne8122 So obvious, but I did not think of it. My idea was way more complicated!
They're almost always left-handed as well...
@@aykoch she is right handed. when she writes, the arm moves away from the body. left hand arm would move toward the body. because the video is flipped, it's a bit of a mind trick to see it.
@@jsonbourne8122 Nice attention to detail as they made sure the outfit was symmetrical without any logos and had a ring on each hand's ring finger, making it harder to tell it was flipped.
The explanation was spot on!
IBM is the go to platform to learn about new technology with their high quality content explained and illustrated with so much simplicity.
Every time I watch one of these videos I'm amazed at the presenter's skill at writing backwards.
The video is flipped
This is a fantastic video to learn about RAG in under 7 minutes. Thank you
Wow, I opened youtube coming from the ibm blog just to leave a comment. Clearly explained, very good example, and well presented as well!! :) Thank you
As a salesperson that actually loves tech. This was an awesome explanation and the fact it was visual helped a ton!!!! Thanks
For me, this is the most easy-to-understand video to explain RAG!
this let's me understand why the embeddings used to generate the vectorstore is a different set from the embeddings of the LLM... Thanks, Marina!
This video is highly underviewed for as informative as it is!
Really comprehensive, well explained Marina Danilevsky !
Best explanation so far from all the content on internet.
One of the easiest to understand RAG explanations I've seen - thanks.
The interesting part is not retrieval from the internet, but retrieval from long term memory, and with a stated objective that builds on such long term memory, and continually gives it "maintenance" so it's efficient and effective to answer. LLMs are awesome because even though there are many challenges ahead, they sort of give us a hint of what's possible, without them it would be hard to have the motivation to follow the road
The explanation is good and easy to understand for a student like me who is new to this topic it gives me a clear idea of what RAG is.
lecturer did a fantastic job. simple and easy to understand.
Great explanation. Even the pros in the field I have never seen explain like this.
I spent all of the 1st watch talking while a friend watched it aswell trying to figure out is she is a robot because of the backwards writing. Good and fast info the 2nd watch. Great job
This was such simple and clear explanation of complex subject. Thanks Marina :)
We also need the models to cross check their own answers with the sources of information before printing out the answer to the user. There is no self control today. Models just say things. "I don't know" is actually a perfectly fine answer sometimes!
Great down the rabbit hole video. Very deep and understandable. IBM academy worthy in my opinion.
Marina has done a great job explaining LLM and RAGs in simple terms.
Amazing explanation. Starting from scratch and gained great perspective on this in a very short time.
I have no "Data Science" background. But I completely understood. You simplified this so unbelievably well. Thanks !
I really like the analogy from the beginning! It was very smooth explanation! Well done!
Thank you for providing a thorough and accessible explanation of RAG!
Did all the speakers have to learn how to write in a mirrored way or is this effect reached by some digital trick?
There is a digital mirroring technique which is used to show the content this way...
She was right handed before the mirror effect
Writing on a clear glass, camera is behind the glass. It's like standing a glass and lookin at a person in an interrogation room
@Helixur you got my answer buddy!! Simple
I have few questions here @ (1) When I prompt and it is not present in context store, shall I get generated text from LLM?
2. when I prompt and a match with embeddings of context store, shall I get content generated from both LLM and Context store?
3. How to enforce RAG framework in Langchain? Appreciate answers
Wow, simple neat and clear explanation!!!
Please keep all these videos coming! They are so easy to understand and straightforward. Muchas gracias!
This explantation is one of the best out there.
Brilliant explanation and illustration. Thanks for your hard work putting this presentation together.
Very precise and exact information on RAG in a nutshell. Thank you for saving my time.
Thats one of the best explaination I have got so far ! Thanks a ton !
Loved this method of explaining concepts. Thank you!
Wow, having a lightbulb moment finally after hearing this mentioned so often. Makes more sense now!
good explanation, it's very easy to understand. this video is the first one when I search RAG on UA-cam. great job ;)
The ability to write backwards, much less cursive writing backwards, is very impressive!
See ibm.biz/write-backwards
Left hand too!
@@IBMTechnology Thanks .... I was reading comments to check for an answer for that question!
That's what Knowledge graphs are for, to keep LLMs grounded with a reliable source and up-to-date.
This is the best explanation I have seen so far for RAG! Amazing content!
Great, simple, quick explanation
Nicely explained. My questions/doubts?
1. Doesn't this raise questions about the process of building and testing LLMs?
2. In such scenarios will the test and training data used be considered authentic and not "limited and biased"?
3. Is there a process/standard on how often the "primary source data" should be updated?
outstanding explenation and lecturer! Well done!
perfect explanation understood every bit , no lags kept it very interesting ,amazing job
That's the best video about RAG that I've watched
Hey, JP here again,
Thank You IBM
Fantastic explanation, proud to be an IBMer
The entire video I've been wondering how they made the transparent whiteboard
Thanks. Great video.
I've had too many conversations where Chatgpt has apparently just made stuff up. I know that's not what happens really, but it seems like it and it still makes untrue statements.
I'm glad researchers are working to improve things.
Exactly what I was trying to understand, great explanation!
Thanks Marina !!! For that such a simple explanation on such a complex topic !!!
I have watched many IBM videos and this is the undoubtedly the best ! I will be searching for your videos now Marina!
Great explaination. It's very helpful for my project a GEN Ai intern
Very well explained and it is easily understandable to non AI person as well. Thanks.
An amazing explanation that made RAG understandable in about 4:23 minutes!
You’re an amazing teacher.
very well executed presentation.
i had to think twice about how you can write in reverse but then i RAGed my system 2 :)
This was such an amazing explanation!
wow this was an amazing Explanation ,very easy to understand
That was excellent, simple, and elegant! Thank you!
tokens as a [word] is what I'm working on right now (solo, self learning LLM techniques), this video helped me realize how the model doesn't know what it's outputting obviously, but AI-AI is different, so building tokens that have dimensional vectors that process in a separate model, can be used for explainable AI.
meaning a separate model processes the response itself, meta, it's for building evolution learning. AI-AI machine learning, you need a way to configure in between the iterations.
Finally, we got a clear explanation!
Fantastic video and explanation. Thank you!
Super good and clear, well done!
Great explanation. Thank you!😊
Amazing talk! Thanks for the sharing!
Great explanation! The video was very didactic, congratulations!
This was explained fantastically.
This is a great explanation. Thank you
the color coding on your whiteboard is really apt here !
Very clear explanation, much respect 🫡
The video is short and consice yet the delivery is very elegant. She might be the best instructor that have teached me. Any idea how the video was created?
Great explanation of RAG. Thank you
Great explanation with an example. Thank you
Thank you, Marina Danilevsky ....
AWESOME EXPLANATION OF THE CONCEPT RAG
This is a fantastic lesson video.
Amazing explanation, finally i understand it.
Pretty simple explanation, thank you
The explanation was very good 💯.
Great video, excellent explanation!
very good and clear explanation
Thank you for such a great explanation.
From which corpus/database are the documents retrieved from? Are they up-to date? and how does it know the best documents to select from a given set?
Beautifully explained....thanks
that reverse writing made be anxious, but a very smart explanation for RAG!!