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

xzskywalkersun

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

vt

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

ericadar

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.

natoreus

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

jordonkash

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

geopopos

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

digvijaysingh

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

TheAllnun

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

ntoscano

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!

aam

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

m.kaschi

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

ReflectionOcean

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

maruthuk

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

ghtgillen

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

Lucildor

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!

jyhherng

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.

hamidapremani

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

GregSolon

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.

vikramn

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

kingvanessa

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