What is Retrieval-Augmented Generation (RAG)?

Рет қаралды 474,350

Күн бұрын

Try RAG with watsonx → ibm.biz/BdMsRT
Learn more about RAG→ ibm.biz/BdMsRt
Large language models usually give great answers, but because they're limited to the training data used to create the model. Over time they can become incomplete--or worse, generate answers that are just plain wrong. One way of improving the LLM results is called "retrieval-augmented generation" or RAG. In this video, IBM Senior Research Scientist Marina Danilevsky explains the LLM/RAG framework and how this combination delivers two big advantages, namely: the model gets the most up-to-date and trustworthy facts, and you can see where the model got its info, lending more credibility to what it generates.
Get started for free on IBM Cloud → ibm.biz/sign-up-now
Subscribe to see more videos like this in the future → ibm.biz/subscribe-now

Пікірлер: 368

@xzskywalkersun515 5 ай бұрын

This lecturer should be given credit for such an amazing explanation.

@cosmicscattering5499 3 ай бұрын

I was thinking the same, she explained this so clearly.

@tariqmking 2 ай бұрын

Yes this was excellently explained, kudos to her.

@brianmi40 Ай бұрын

Or at least credit for being able to write backwards!

@victoriamilhoan512 9 күн бұрын

The connection between a human answering a question in real life vs how LLMs (with or without RAG) do it was so helpful!

@vt1454 6 ай бұрын

IBM should start a learning platform. Their videos are so good.

@XEQUTE 5 ай бұрын

i think they already do

@srinivasreddyt9555 Ай бұрын

Yes, they have it already. KZfaq.

@siddheshpgaikwad 28 күн бұрын

Its mirrored video, she wrote naturally and video was mirrored later

@Hossam_Ahmed_ 27 күн бұрын

They have skill build but not videos at least most of the content

@CaptPicard81 25 күн бұрын

They do, I recently attended a week long AI workshop based on an IBM curriculum

@ghtgillen 7 ай бұрын

Your ability to write backwards on the glass is amazing! ;-)

@jsonbourne8122 6 ай бұрын

They flip the video

@Paul-rs4gd 3 ай бұрын

@@jsonbourne8122 So obvious, but I did not think of it. My idea was way more complicated!

@natoreus 6 күн бұрын

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.

@jordonkash 3 ай бұрын

4:15 Marina combines the colors of the word prompt to emphasis her point. Nice touch

@geopopos 2 ай бұрын

I love seeing a large company like IBM invest in educating the public with free content! You all rock!

@ericadar 5 ай бұрын

Marina is a talented teacher. This was brief, clear and enjoyable.

@maruthuk 7 ай бұрын

Loved the simple example to describe how RAG can be used to augment the responses of LLM models.

@vikramn2190 8 ай бұрын

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.

@DJ-lo8qj 28 күн бұрын

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)

@PlaytimeEntertainment 26 күн бұрын

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

@TheAllnun21 5 ай бұрын

Wow, this is the best beginner's introduction I've seen on RAG!

@aam50 5 ай бұрын

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!

@ntoscano01 4 ай бұрын

Very well explained!!! Thank you for your explanation of this. I’m so tired of 45 minute KZfaq 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.

@m.kaschi2741 5 ай бұрын

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

@Lucildor 3 ай бұрын

Please keep all these videos coming! They are so easy to understand and straightforward. Muchas gracias!

@Shailendrashail 8 ай бұрын

Good Explanation of RAG. Thanks for sharing.

@javi_park 3 ай бұрын

hold up - the fact that the board is flipped is the most underrated modern education marvel nobody's talking about

@RiaKeenan 3 ай бұрын

I know, right?!

@euseikodak 3 ай бұрын

Probably they filmed it in front of a glass board and flipped the video on edition later on

@politicallyincorrect1705 3 ай бұрын

Filmed in front of a non-reflective mirror.

@TheTomtz Ай бұрын

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🤣

@thewallstreetjournal5675 Ай бұрын

Is the board fliped or has she been flipped?

@GregSolon 3 ай бұрын

One of the easiest to understand RAG explanations I've seen - thanks.

@kingvanessa946 3 ай бұрын

For me, this is the most easy-to-understand video to explain RAG!

@jyhherng 6 ай бұрын

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!

@projectfocrin 5 ай бұрын

Great explanation. Even the pros in the field I have never seen explain like this.

@paulaenchina 4 ай бұрын

This is the best explanation I have seen so far for RAG! Amazing content!

@TheMsksk 8 ай бұрын

Great video as always. Thanks for sharing.

@past_life_project 3 ай бұрын

I have watched many IBM videos and this is the undoubtedly the best ! I will be searching for your videos now Marina!

@ReflectionOcean 5 ай бұрын

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

@444Yielding 27 күн бұрын

This video is highly underviewed for as informative as it is!

@hamidapremani6151 2 ай бұрын

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.

@rujmah 2 ай бұрын

Brilliant explanation and illustration. Thanks for your hard work putting this presentation together.

@redwinsh258 6 ай бұрын

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

@francischacko3627 24 күн бұрын

perfect explanation understood every bit , no lags kept it very interesting ,amazing job

@vnaykmar7 5 ай бұрын

Such an amazing explanation. Thank you ma'am!

@HimalayJoriwal 2 ай бұрын

Best explanation so far from all the content on internet.

@afshinkarimi2382 8 ай бұрын

Great video. Thanks for sharing

@rvssrkrishna2 2 ай бұрын

Very precise and exact information on RAG in a nutshell. Thank you for saving my time.

@preciousrose2715 Ай бұрын

This was such an amazing explanation!

@ashwinkumar675 23 күн бұрын

This is so well explained! Thank you 👍🏻✅

@user-cd6hp5kc1n 7 ай бұрын

The ability to write backwards, much less cursive writing backwards, is very impressive!

@IBMTechnology 7 ай бұрын

See ibm.biz/write-backwards

@jsonbourne8122 6 ай бұрын

Left hand too!

@NishanSaliya 5 ай бұрын

@@IBMTechnology Thanks .... I was reading comments to check for an answer for that question!

@Anubis2828 2 ай бұрын

Great, simple, quick explanation

@evaiintelligence 28 күн бұрын

Marina has done a great job explaining LLM and RAGs in simple terms.

@bdouglas Ай бұрын

That was excellent, simple, and elegant! Thank you!

@mstarlingc 5 ай бұрын

Pretty simple explanation, thank you

@rsu82 3 күн бұрын

good explanation, it's very easy to understand. this video is the first one when I search RAG on KZfaq. great job ;)

@alexiojunior7867 Ай бұрын

wow this was an amazing Explanation ,very easy to understand

@sawyerburnett8319 4 ай бұрын

Wow, having a lightbulb moment finally after hearing this mentioned so often. Makes more sense now!

@aniket_mishr Ай бұрын

The explanation was very good 💯.

@rafa1rafa 5 ай бұрын

Great explanation! The video was very didactic, congratulations!

@kunalsoni7681 6 ай бұрын

Thanks for letting us know about this feature of LLM :)

@Aryankingz 7 ай бұрын

That's what Knowledge graphs are for, to keep LLMs grounded with a reliable source and up-to-date.

@toenytv7946 2 ай бұрын

Great down the rabbit hole video. Very deep and understandable. IBM academy worthy in my opinion.

@PaulGrew-wl7mh Ай бұрын

An amazing explanation that made RAG understandable in about 4:23 minutes!

@deltawhiplash1614 12 күн бұрын

This is a really good video thank you for sharing this knowledge

@JasonVonHolmes 2 ай бұрын

This was explained fantastically.

@user-im6ub3sf6m 3 ай бұрын

Great explanation with an example. Thank you

@khalidelgazzar 5 ай бұрын

Great explanation. Thank you!😊

@siddharth4251 Ай бұрын

Amazing explanation, finally i understand it.

@user-hk5dk9rb6p 4 ай бұрын

Fantastic video and explanation. Thank you!

@prasannakulkarni5664 Ай бұрын

the color coding on your whiteboard is really apt here !

@oieieio741 5 ай бұрын

Very Helpful! Great explanation. thx IBM

@zuzukouzina-original 3 ай бұрын

Very clear explanation, much respect 🫡

@421sap 6 ай бұрын

Thank you, Marina Danilevsky ....

@star2k279 4 ай бұрын

Thank you for such a great explanation.

@rockochamp 5 ай бұрын

very well executed presentation. i had to think twice about how you can write in reverse but then i RAGed my system 2 :)

@lauther_27 5 ай бұрын

Amazing video, thanks IBM ❤

@janhorak8799 2 ай бұрын

Did all the speakers have to learn how to write in a mirrored way or is this effect reached by some digital trick?

@VlogBySKSK Ай бұрын

There is a digital mirroring technique which is used to show the content this way...

@mao-tse-tung 24 күн бұрын

She was right handed before the mirror effect

@shashankshekharsingh9336 17 күн бұрын

very good and clear explanation

@Kekko400D 3 ай бұрын

Fantastic explanation, proud to be an IBMer

@eddisonlewis8099 3 ай бұрын

AWESOME EXPLANATION OF THE CONCEPT RAG

@xdevs23 2 ай бұрын

The entire video I've been wondering how they made the transparent whiteboard

@rahulberry4806 20 күн бұрын

thanks for the great explanation

@gaemrpaterso-ri2jd 8 ай бұрын

Great video, you guys should do one on promising tech industries

@sudhakarveeraraghavan5832 Ай бұрын

Very well explained and it is easily understandable to non AI person as well. Thanks.

@laurentpastorelli1354 4 ай бұрын

Super good and clear, well done!

@AdarshKumar-kx2cn 3 ай бұрын

Beautifully explained....thanks

@johnmccullough7084 6 ай бұрын

Appreciate the succinct explanation. 👍

@thomasbrowne6649 Ай бұрын

This is excellent and I hope IBM does well in this space. We need a reliable, non-hype vendor.

@AntenorTeixeira 5 ай бұрын

That's the best video about RAG that I've watched

@aneesarom Күн бұрын

Best explanation ever

@ericmcnally5128 2 ай бұрын

This is a fantastic lesson video.

@user-bo1kv5zy3w 7 ай бұрын

Awesome explanation. Love you.

@mayankbumb7272 7 күн бұрын

Great explanation

@randomforest_dev 28 күн бұрын

Very good explanation!

@sumedhaj9017 2 ай бұрын

Amazing explanation! Thank you:)

@sk-6032 9 күн бұрын

Very well explained 🙏🏼👍

@mohamadhijazi3895 Ай бұрын

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?

@carvalhoribeiro Ай бұрын

Amazing work. Thanks for sharing this.

@AC-xd7sw 4 ай бұрын

Insightful, please more video like this

@zhanezar Ай бұрын

BRILLIANT VIDEO thank you!

@ayanSaha13291 28 күн бұрын

Great video! thanks for educating!

@stanislavzayarsky 3 ай бұрын

Finally, we got a clear explanation!

@user-xf4vm2gf6g 3 ай бұрын

Excellent ! thank you for sharing this knowledge !

@Junglytics 3 ай бұрын

Great video, excellent explanation!

@terencelewis4985 3 ай бұрын

Excellent explanation!

@yashkhorania3726 15 күн бұрын

very nicely explained

@MraM23 3 ай бұрын

Great lessons! Nice of you to step out 🙃 and make such engaging and educative content This is a very useful in helping us in critical thinking. Thank you for sharing this video. 👍 Current ai models may impose neurotypical norms and expectations based on current data trained on . 🤔 Curious to see more on how IBM approach the challenges and limitations of Ai

@sharingmatters 2 ай бұрын

Well explained!

@sprintwithcarlos 6 ай бұрын

Great explanation!

@kallamamran 4 ай бұрын

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!