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In-Ear Insights: What is Retrieval Augmented Generation (RAG)?
In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss Retrieval Augmented Generation (RAG). You’ll learn what RAG is and how it can significantly improve the accuracy and relevance of AI responses by using your own data. You’ll understand the crucial differences between RAG and typical search engines or generative AI models, clarifying when RAG is truly needed. You’ll discover practical examples of when RAG becomes essential, especially for handling sensitive company information and proprietary knowledge. Tune in to learn when and how RAG can be a game-changer for your data strategy and when simpler AI tools will suffice!
Watch the video here:
Can’t see anything? Watch it on YouTube here.
Listen to the audio here:
https://traffic.libsyn.com/inearinsights/tipodcast-what-is-retrieval-augmented-generation-rag.mp3
Download the MP3 audio here.
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Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher S. Penn – 00:00
In this week’s In Ear Insights, let’s…
Christopher S. Penn – 00:02
Talk about RAG—Retrieval augmented generation.
Christopher S. Penn – 00:06
What is it?
Christopher S. Penn – 00:07
Why do we care about it?
Christopher S. Penn – 00:09
So Katie, I know you’re going in kind of blind on this. What do you know about retrieval augmented generation?
Katie Robbert – 00:17
I knew we were going to be talking about this, but I purposely didn’t do any research because I wanted to see how much I thought I understood already just based on. So if I take apart just even the words Retrieval augmented generation, I think retrieval means it has…
Katie Robbert – 00:41
To go find something augmented, meaning it’s…
Katie Robbert – 00:44
Going to add on to something existing and then generation means it’s going to do something. So it’s going to find data added on to the whatever is existing, whatever that is, and then create something. So that’s my basic. But obviously, that doesn’t mean anything. So we have to put it in…
Katie Robbert – 01:05
The context of generative AI.
Katie Robbert – 01:07
So what am I missing?
Christopher S. Penn – 01:09
Believe it or not, you’re not missing a whole lot. That’s actually a good encapsulation. Happy Monday. Retrieval augmented generation is a system for bringing in contextual knowledge to a prompt so that generative AI can do a better job.
Probably one of the most well-known and easiest-to-use systems like this is Google’s free NotebookLM where you just put in a bunch of documents. It does all the work—the technical stuff of tokenization and embeddings and all that stuff. And then you can chat with your documents and say, ‘Well, what’s in this?’
In our examples, we’ve
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By Trust Insights
5
99 ratings
In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss Retrieval Augmented Generation (RAG). You’ll learn what RAG is and how it can significantly improve the accuracy and relevance of AI responses by using your own data. You’ll understand the crucial differences between RAG and typical search engines or generative AI models, clarifying when RAG is truly needed. You’ll discover practical examples of when RAG becomes essential, especially for handling sensitive company information and proprietary knowledge. Tune in to learn when and how RAG can be a game-changer for your data strategy and when simpler AI tools will suffice!
Watch the video here:
Can’t see anything? Watch it on YouTube here.
Listen to the audio here:
https://traffic.libsyn.com/inearinsights/tipodcast-what-is-retrieval-augmented-generation-rag.mp3
Download the MP3 audio here.
[podcastsponsor]
Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher S. Penn – 00:00
In this week’s In Ear Insights, let’s…
Christopher S. Penn – 00:02
Talk about RAG—Retrieval augmented generation.
Christopher S. Penn – 00:06
What is it?
Christopher S. Penn – 00:07
Why do we care about it?
Christopher S. Penn – 00:09
So Katie, I know you’re going in kind of blind on this. What do you know about retrieval augmented generation?
Katie Robbert – 00:17
I knew we were going to be talking about this, but I purposely didn’t do any research because I wanted to see how much I thought I understood already just based on. So if I take apart just even the words Retrieval augmented generation, I think retrieval means it has…
Katie Robbert – 00:41
To go find something augmented, meaning it’s…
Katie Robbert – 00:44
Going to add on to something existing and then generation means it’s going to do something. So it’s going to find data added on to the whatever is existing, whatever that is, and then create something. So that’s my basic. But obviously, that doesn’t mean anything. So we have to put it in…
Katie Robbert – 01:05
The context of generative AI.
Katie Robbert – 01:07
So what am I missing?
Christopher S. Penn – 01:09
Believe it or not, you’re not missing a whole lot. That’s actually a good encapsulation. Happy Monday. Retrieval augmented generation is a system for bringing in contextual knowledge to a prompt so that generative AI can do a better job.
Probably one of the most well-known and easiest-to-use systems like this is Google’s free NotebookLM where you just put in a bunch of documents. It does all the work—the technical stuff of tokenization and embeddings and all that stuff. And then you can chat with your documents and say, ‘Well, what’s in this?’
In our examples, we’ve
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