What is Retrieval-Augmented Generation (RAG)?

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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.
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[@xzskywalkersun515]

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

[@cosmicscattering5499]

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

[@tariqmking]

Yes this was excellently explained, kudos to her.

[@brianmi40]

Or at least credit for being able to write backwards!

[@victoriamilhoan512]

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

[@vt1454]

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

[@XEQUTE]

i think they already do

[@srinivasreddyt9555]

Yes, they have it already. KZbin.

[@siddheshpgaikwad]

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

[@Hossam_Ahmed_]

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

[@CaptPicard81]

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

[@ghtgillen]

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

[@jsonbourne8122]

They flip the video

[@Paul-rs4gd]

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

[@natoreus]

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]

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

[@maruthuk]

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

[@ericadar]

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

[@TheAllnun21]

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

[@m.kaschi2741]

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

[@geopopos]

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

[@vikramn2190]

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]

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]

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

[@Lucildor]

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

[@GregSolon]

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

[@aam50]

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]

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

[@444Yielding]

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

[@kingvanessa946]

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

[@projectfocrin]

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

[@Shailendrashail]

Good Explanation of RAG. Thanks for sharing.

[@jyhherng]

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!

[@paulaenchina]

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

[@ReflectionOcean]

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

[@past_life_project]

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

[@rujmah]

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

[@TheMsksk]

Great video as always. Thanks for sharing.

[@hamidapremani6151]

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.

[@redwinsh258]

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

[@evaiintelligence]

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

[@vnaykmar7]

Such an amazing explanation. Thank you ma'am!

[@francischacko3627]

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

[@gaemrpaterso-ri2jd]

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

[@rvssrkrishna2]

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

[@user-cd6hp5kc1n]

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

[@IBMTechnology]

See ibm.biz/write-backwards

[@jsonbourne8122]

Left hand too!

[@NishanSaliya]

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

[@afshinkarimi2382]

Great video. Thanks for sharing

[@rafa1rafa]

Great explanation! The video was very didactic, congratulations!

[@kunalsoni7681]

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

[@Anubis2828]

Great, simple, quick explanation

[@HimalayJoriwal]

Best explanation so far from all the content on internet.

[@bdouglas]

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

[@mstarlingc]

Pretty simple explanation, thank you

[@Aryankingz]

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

[@sawyerburnett8319]

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

[@PaulGrew-wl7mh]

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

[@rockochamp]

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

[@toenytv7946]

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

[@javi_park]

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

[@RiaKeenan]

I know, right?!

[@euseikodak]

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

[@politicallyincorrect1705]

Filmed in front of a non-reflective mirror.

[@TheTomtz]

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]

Is the board fliped or has she been flipped?

[@xdevs23]

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

[@user-im6ub3sf6m]

Great explanation with an example. Thank you

[@thomasbrowne6649]

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

[@preciousrose2715]

This was such an amazing explanation!

[@mohamadhijazi3895]

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?

[@user-hk5dk9rb6p]

Fantastic video and explanation. Thank you!

[@laurentpastorelli1354]

Super good and clear, well done!

[@oieieio741]

Very Helpful! Great explanation. thx IBM

[@eddisonlewis8099]

AWESOME EXPLANATION OF THE CONCEPT RAG

[@Kekko400D]

Fantastic explanation, proud to be an IBMer

[@ashwinkumar675]

This is so well explained! Thank you 👍🏻✅

[@alexiojunior7867]

wow this was an amazing Explanation ,very easy to understand

[@JasonVonHolmes]

This was explained fantastically.

[@zuzukouzina-original]

Very clear explanation, much respect 🫡

[@khalidelgazzar]

Great explanation. Thank you!😊

[@lauther_27]

Amazing video, thanks IBM ❤

[@johnmccullough7084]

Appreciate the succinct explanation. 👍

[@AntenorTeixeira]

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

[@MraM23]

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

[@user-bo1kv5zy3w]

Awesome explanation. Love you.

[@ericmcnally5128]

This is a fantastic lesson video.

[@prasannakulkarni5664]

the color coding on your whiteboard is really apt here !

[@aniket_mishr]

The explanation was very good 💯.

[@kallamamran]

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!

[@AC-xd7sw]

Insightful, please more video like this

[@deltawhiplash1614]

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

[@star2k279]

Thank you for such a great explanation.

[@Junglytics]

Great video, excellent explanation!

[@terencelewis4985]

Excellent explanation!

[@AdarshKumar-kx2cn]

Beautifully explained....thanks

[@sprintwithcarlos]

Great explanation!

[@katsunoi]

nice video - great explanation!

[@sumedhaj9017]

Amazing explanation! Thank you:)

[@421sap]

Thank you, Marina Danilevsky ....

[@janhorak8799]

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

[@VlogBySKSK]

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

[@mao-tse-tung]

She was right handed before the mirror effect

[@user-xf4vm2gf6g]

Excellent ! thank you for sharing this knowledge !

[@stanislavzayarsky]

Finally, we got a clear explanation!

[@sudhakarveeraraghavan5832]

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

[@shashankshekharsingh9336]

very good and clear explanation

[@berkeokur99]

Love the neon markers, also the content of course

[@mohammadsubhan1318]

Nicely explained 👍

[@siddharth4251]

Amazing explanation, finally i understand it.

[@MarshallMelnychuk]

Thank you Marina, very helpful and informative video. One question I have is; how do you make these videos like this? Being able to on a screen facing the camera, this is great. What's your secret?

[@PeterCooperUK]

Sometimes these are done on transparent "whiteboards" and the video is then flipped horizontally.

[@ChristopherSmithGPlus]

kzbin.info/www/bejne/m4eygXeHarCMqdEsi=LADnROL0SF33Hg54

[@IBMTechnology]

See ibm.biz/write-backwards

[@lowkeyproducktvt2101]

@@IBMTechnology okay now i get it !!!

[@rayuduaddagarla3857]

IBM should hire left hand writers so it will right handed after flip 😊

[@carvalhoribeiro]

Amazing work. Thanks for sharing this.

[@johanhumblet6090]

very well explained!

[@shinemuphy]

Excellent explanation. thx

[@rahulberry4806]

thanks for the great explanation

[@mrhassell]

RAG combines the generative power of LLMs with the precision of specialized data search mechanisms, resulting in nuanced and contextually relevant responses.

[@mayankbumb7272]

Great explanation

[@kevinmulligan2006]

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.

[@kevinmulligan2006]

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.

[@randomforest_dev]

Very good explanation!

[@sk-6032]

Very well explained 🙏🏼👍

[@gbluemink]

So the question I have here is when I have an answer from my LLM but not the Rag data, what is the response to the user? "I don't know" or the LLM response that may be out of date or without a reliable source? Looks like a question for an LLM :)

[@vic3sax972]

Nice explanation