What is Retrieval-Augmented Generation (RAG)?

Поділитися
Вставка
  • Опубліковано 29 гру 2024

КОМЕНТАРІ • 551

  • @xzskywalkersun515
    @xzskywalkersun515 Рік тому +927

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

    • @cosmicscattering5499
      @cosmicscattering5499 11 місяців тому +8

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

    • @tariqmking
      @tariqmking 9 місяців тому +4

      Yes this was excellently explained, kudos to her.

    • @brianmi40
      @brianmi40 9 місяців тому +19

      Or at least credit for being able to write backwards!

    • @victoriamilhoan512
      @victoriamilhoan512 7 місяців тому +3

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

    • @aguiremedia
      @aguiremedia 7 місяців тому +1

      Why. Chat gpt wrote it

  • @vt1454
    @vt1454 Рік тому +545

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

    • @XEQUTE
      @XEQUTE Рік тому +10

      i think they already do

    • @srinivasreddyt9555
      @srinivasreddyt9555 9 місяців тому

      Yes, they have it already. UA-cam.

    • @siddheshpgaikwad
      @siddheshpgaikwad 8 місяців тому +4

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

    • @Hossam_Ahmed_
      @Hossam_Ahmed_ 8 місяців тому

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

    • @CaptPicard81
      @CaptPicard81 8 місяців тому

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

  • @geopopos
    @geopopos 9 місяців тому +93

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

    • @theupsider
      @theupsider 16 днів тому

      Apparently there are scientists in charge who are pushing for such an agenda. Love to see it.

  • @jordonkash
    @jordonkash 10 місяців тому +91

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

  • @ericadar
    @ericadar Рік тому +104

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

  • @ReflectionOcean
    @ReflectionOcean Рік тому +31

    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

  • @ntoscano01
    @ntoscano01 11 місяців тому +35

    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.

  • @TheAllnun21
    @TheAllnun21 Рік тому +30

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

  • @natoreus
    @natoreus 7 місяців тому +25

    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.

  • @digvijaysingh6882
    @digvijaysingh6882 6 місяців тому +16

    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

  • @AlexandraSteskal
    @AlexandraSteskal 4 місяці тому +3

    I love IBM teachers/trainers, I used to work at IBM and their in-house education quality was AMAZING!

  • @vikramn2190
    @vikramn2190 Рік тому +45

    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.

    • @judahb3ar
      @judahb3ar 8 місяців тому

      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
      @PlaytimeEntertainment 8 місяців тому +3

      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

    • @velocityra
      @velocityra 7 місяців тому

      Thank you. So many people praising this even though it didn't explain anything that can't be googled in 2 seconds.

  • @aam50
    @aam50 Рік тому +20

    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!

  • @javi_park
    @javi_park 11 місяців тому +68

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

    • @RiaKeenan
      @RiaKeenan 11 місяців тому

      I know, right?!

    • @euseikodak
      @euseikodak 11 місяців тому +8

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

    • @politicallyincorrect1705
      @politicallyincorrect1705 10 місяців тому +1

      Filmed in front of a non-reflective mirror.

    • @TheTomtz
      @TheTomtz 9 місяців тому +2

      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
      @thewallstreetjournal5675 9 місяців тому +1

      Is the board fliped or has she been flipped?

  • @maruthuk
    @maruthuk Рік тому +22

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

  • @ghtgillen
    @ghtgillen Рік тому +76

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

    • @jsonbourne8122
      @jsonbourne8122 Рік тому +35

      They flip the video

    • @Paul-rs4gd
      @Paul-rs4gd 11 місяців тому +12

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

    • @aykoch
      @aykoch 7 місяців тому +3

      They're almost always left-handed as well...

    • @7th_CAV_Trooper
      @7th_CAV_Trooper 7 місяців тому +11

      @@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.

    • @bikrrr
      @bikrrr 6 місяців тому +1

      ​@@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.

  • @hamidapremani6151
    @hamidapremani6151 10 місяців тому +2

    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.

  • @ltkbeast
    @ltkbeast 2 місяці тому +2

    Every time I watch one of these videos I'm amazed at the presenter's skill at writing backwards.

  • @AnjanaSilvaAJ
    @AnjanaSilvaAJ Місяць тому +1

    This is a fantastic video to learn about RAG in under 7 minutes. Thank you

  • @m.kaschi2741
    @m.kaschi2741 Рік тому +8

    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

  • @Will-lg9ev
    @Will-lg9ev 6 місяців тому +1

    As a salesperson that actually loves tech. This was an awesome explanation and the fact it was visual helped a ton!!!! Thanks

  • @kingvanessa946
    @kingvanessa946 11 місяців тому +1

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

  • @jyhherng
    @jyhherng Рік тому +6

    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!

  • @444Yielding
    @444Yielding 8 місяців тому +3

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

  • @Linkky
    @Linkky Місяць тому

    Really comprehensive, well explained Marina Danilevsky !

  • @HimalayJoriwal
    @HimalayJoriwal 10 місяців тому

    Best explanation so far from all the content on internet.

  • @GregSolon
    @GregSolon 10 місяців тому

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

  • @redwinsh258
    @redwinsh258 Рік тому +23

    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

  • @shreyjain3344
    @shreyjain3344 4 місяці тому

    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.

  • @ivlivs.c3666
    @ivlivs.c3666 6 місяців тому

    lecturer did a fantastic job. simple and easy to understand.

  • @projectfocrin
    @projectfocrin Рік тому +5

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

  • @damen238
    @damen238 11 днів тому

    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

  • @AbhishekVerma-jw3jg
    @AbhishekVerma-jw3jg 4 місяці тому

    This was such simple and clear explanation of complex subject. Thanks Marina :)

  • @kallamamran
    @kallamamran Рік тому +5

    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!

  • @toenytv7946
    @toenytv7946 9 місяців тому +1

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

  • @evaiintelligence
    @evaiintelligence 8 місяців тому

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

  • @batumanav
    @batumanav 2 місяці тому

    Amazing explanation. Starting from scratch and gained great perspective on this in a very short time.

  • @rajeshseptember09
    @rajeshseptember09 6 місяців тому

    I have no "Data Science" background. But I completely understood. You simplified this so unbelievably well. Thanks !

  • @EmmettYoung
    @EmmettYoung 4 місяці тому

    I really like the analogy from the beginning! It was very smooth explanation! Well done!

  • @LindsayRichardson-rv2wn
    @LindsayRichardson-rv2wn 4 місяці тому

    Thank you for providing a thorough and accessible explanation of RAG!

  • @janhorak8799
    @janhorak8799 9 місяців тому +27

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

    • @VlogBySKSK
      @VlogBySKSK 8 місяців тому

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

    • @mao-tse-tung
      @mao-tse-tung 8 місяців тому +6

      She was right handed before the mirror effect

    • @Helixur
      @Helixur 7 місяців тому +1

      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

    • @vipulsonawane7508
      @vipulsonawane7508 12 днів тому

      @Helixur you got my answer buddy!! Simple

  • @ssr142812
    @ssr142812 6 місяців тому

    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

  • @vipulsonawane7508
    @vipulsonawane7508 12 днів тому

    Wow, simple neat and clear explanation!!!

  • @ReelTaino
    @ReelTaino 11 місяців тому

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

  • @jean-charles-AI
    @jean-charles-AI 5 місяців тому +1

    This explantation is one of the best out there.

  • @rujmah
    @rujmah 10 місяців тому

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

  • @rvssrkrishna2
    @rvssrkrishna2 10 місяців тому

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

  • @rhitikkrishnani510
    @rhitikkrishnani510 4 місяці тому

    Thats one of the best explaination I have got so far ! Thanks a ton !

  • @WallyAlcacio
    @WallyAlcacio Місяць тому

    Loved this method of explaining concepts. Thank you!

  • @sawyerburnett8319
    @sawyerburnett8319 11 місяців тому +1

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

  • @rsu82
    @rsu82 7 місяців тому

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

  • @JonCoulter-u1y
    @JonCoulter-u1y Рік тому +16

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

    • @IBMTechnology
      @IBMTechnology  Рік тому +9

      See ibm.biz/write-backwards

    • @jsonbourne8122
      @jsonbourne8122 Рік тому

      Left hand too!

    • @NishanSaliya
      @NishanSaliya Рік тому

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

  • @Aryankingz
    @Aryankingz Рік тому +4

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

  • @paulaenchina
    @paulaenchina 11 місяців тому +1

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

  • @Anubis2828
    @Anubis2828 9 місяців тому

    Great, simple, quick explanation

  • @sarangag
    @sarangag 2 місяці тому

    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?

  • @peterciank
    @peterciank 4 місяці тому

    outstanding explenation and lecturer! Well done!

  • @francischacko3627
    @francischacko3627 8 місяців тому

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

  • @AntenorTeixeira
    @AntenorTeixeira Рік тому

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

  • @Jaimin_Bariya
    @Jaimin_Bariya 23 дні тому +1

    Hey, JP here again,
    Thank You IBM

  • @Kekko400D
    @Kekko400D 10 місяців тому

    Fantastic explanation, proud to be an IBMer

  • @xdevs23
    @xdevs23 9 місяців тому +7

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

  • @paulw4259
    @paulw4259 14 днів тому

    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.

  • @bhaskarmothali
    @bhaskarmothali 6 місяців тому

    Exactly what I was trying to understand, great explanation!

  • @Bikashics
    @Bikashics 5 місяців тому

    Thanks Marina !!! For that such a simple explanation on such a complex topic !!!

  • @past_life_project
    @past_life_project 11 місяців тому

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

  • @limitlessrari1
    @limitlessrari1 6 місяців тому

    Great explaination. It's very helpful for my project a GEN Ai intern

  • @sudhakarveeraraghavan5832
    @sudhakarveeraraghavan5832 8 місяців тому

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

  • @PaulGrew-wl7mh
    @PaulGrew-wl7mh 9 місяців тому +1

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

  • @mikezooper
    @mikezooper 6 місяців тому

    You’re an amazing teacher.

  • @rockochamp
    @rockochamp Рік тому +1

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

  • @preciousrose2715
    @preciousrose2715 8 місяців тому

    This was such an amazing explanation!

  • @alexiojunior7867
    @alexiojunior7867 8 місяців тому

    wow this was an amazing Explanation ,very easy to understand

  • @bdouglas
    @bdouglas 8 місяців тому

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

  • @neotower420
    @neotower420 9 місяців тому

    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.

    • @neotower420
      @neotower420 9 місяців тому

      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.

  • @stanislavzayarsky
    @stanislavzayarsky 11 місяців тому

    Finally, we got a clear explanation!

  • @SharieffMansour
    @SharieffMansour 11 місяців тому +1

    Fantastic video and explanation. Thank you!

  • @laurentpastorelli1354
    @laurentpastorelli1354 Рік тому +1

    Super good and clear, well done!

  • @khalidelgazzar
    @khalidelgazzar Рік тому +2

    Great explanation. Thank you!😊

  • @keyanguo4912
    @keyanguo4912 3 місяці тому

    Amazing talk! Thanks for the sharing!

  • @rafa1rafa
    @rafa1rafa Рік тому +2

    Great explanation! The video was very didactic, congratulations!

  • @JasonVonHolmes
    @JasonVonHolmes 9 місяців тому

    This was explained fantastically.

  • @user20517
    @user20517 Місяць тому

    This is a great explanation. Thank you

  • @prasannakulkarni5664
    @prasannakulkarni5664 8 місяців тому

    the color coding on your whiteboard is really apt here !

  • @zuzukouzina-original
    @zuzukouzina-original 11 місяців тому

    Very clear explanation, much respect 🫡

  • @mohamadhijazi3895
    @mohamadhijazi3895 8 місяців тому

    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?

  • @oklahomaguy23
    @oklahomaguy23 6 місяців тому

    Great explanation of RAG. Thank you

  • @SandeepDesai-j2w
    @SandeepDesai-j2w 11 місяців тому

    Great explanation with an example. Thank you

  • @421sap
    @421sap Рік тому

    Thank you, Marina Danilevsky ....

  • @eddisonlewis8099
    @eddisonlewis8099 11 місяців тому

    AWESOME EXPLANATION OF THE CONCEPT RAG

  • @ericmcnally5128
    @ericmcnally5128 10 місяців тому

    This is a fantastic lesson video.

  • @siddharth4251
    @siddharth4251 9 місяців тому

    Amazing explanation, finally i understand it.

  • @mstarlingc
    @mstarlingc Рік тому +1

    Pretty simple explanation, thank you

  • @aniket_mishr
    @aniket_mishr 8 місяців тому

    The explanation was very good 💯.

  • @Junglytics
    @Junglytics 11 місяців тому

    Great video, excellent explanation!

  • @shashankshekharsingh9336
    @shashankshekharsingh9336 8 місяців тому

    very good and clear explanation

  • @star2k279
    @star2k279 11 місяців тому +1

    Thank you for such a great explanation.

  • @neutron417
    @neutron417 Рік тому +2

    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?

  • @AdarshKumar-kx2cn
    @AdarshKumar-kx2cn 10 місяців тому

    Beautifully explained....thanks

  • @VishalSharma-gp6dm
    @VishalSharma-gp6dm 9 місяців тому

    that reverse writing made be anxious, but a very smart explanation for RAG!!