What is RAG? (Retrieval Augmented Generation)
Summary
TLDRThe video explains retrieval augmented generation (RAG), a popular AI architecture that allows large language models to answer questions using an organization's proprietary content. It works by breaking down an organization's content into chunks, vectorizing those chunks to enable finding the most relevant passages, and packaging those passages with the user's question into a prompt that is fed to the language model to generate an answer. RAG leverages the power of language models while tailoring their responses to an organization's unique content. The video aims to clarify this increasingly common AI pattern that creates chatbot-like experiences for employees and customers.
Takeaways
- 😀 RAG stands for Retrieval Augmented Generation and is a popular AI solution pattern
- 📚 RAG leverages large language models to create chatbot-like experiences using your own content
- 🤔 RAG works by retrieving the most relevant content from your documents/website to augment the language model's generation
- 🔎 Your content is broken into chunks which are vectorized - this allows finding content relevant to the user's question
- ⚙️ The relevant content chunks are added to the prompt sent to the language model to provide context for answering the question
- 🔢 Vector similarities between the user question and content chunks allow retrieving the most relevant information
- 🗣 A prompt with instructions, relevant content chunks, and the user question is sent to the language model to generate an answer
- 💡 RAG provides a better experience than just listing relevant documents - it creates a new tailored answer
- ✨ Most large language model projects leverage some form of RAG architecture to work with custom content
- 📝 Prompt engineering is important for providing good instructions and content to the language model
Q & A
What is the key benefit of using a rag architecture?
-A rag architecture allows you to leverage the power of large language models to answer questions and generate content, but using your own proprietary content rather than just what's available on the public internet.
What are some common use cases for a rag system?
-Common use cases include customer chatbots, internal company FAQs, documentation search, and automated ticket resolution by searching past tickets and solutions.
How does the system know which content is relevant to the user's question?
-The system vectorizes all the content, giving each chunk a vector of numbers representing its essence. The user's question is also vectorized. Then the vectors are compared to find the chunks most similar to the question vector.
What is a prompt in this context and why is it important?
-A prompt provides instructions and context to the language model. Careful prompt engineering, especially the content before the actual question, helps the model generate better, more tailored responses.
What types of content can I use in a rag system?
-You can use any text content - website pages, PDF documents, internal databases, past tickets or cases, etc. The system will index and vectorize the content for easy retrieval.
Does the rag system replace search engines and knowledge bases?
-No, rag systems complement search and knowledge bases by taking content, aggregating it, and generating tailored responses. So they enhance rather than replace existing systems.
What are vectors in this context and how are they created?
-Vectors are numeric representations of chunks of text, encoding their semantic meaning. They are generated by passing content through a language model encoder which outputs the vector.
What components make up a rag system?
-The main components are the content indexer, vector database, retriever, language model (encoder and decoder), and prompt generator.
Is a separate language model required for encoding versus decoding?
-No, the same language model can be used for both encoding content to vectors and decoding prompts to generate responses. But separate models can be used as well.
How much content is needed to create an effective rag system?
-There's no fixed amount - start with whatever content you have. The more high-quality, relevant content you can provide, the better the generated responses will be.
Outlines
😃 Introducing rag architecture for leveraging large language models on custom content
The paragraph introduces the idea of retrieval augmented generation (rag) architecture, which allows using large language models to generate answers and content based on a company's own custom documents and data sources, similar to how ChatGPT leverages internet content. It explains why rag is popular for building chatbots, QA systems, and more on top of proprietary data.
😉 Explaining the components of a rag system
The paragraph provides an overview of the different components that make up a rag system, including the user question, content sources like a website or documents, instructions that frame the context for the language model, relevant content extraction using vector similarities, assembling the prompt with extracted content, sending to the language model, and getting the final generated response.
🤓 How content chunks are vectorized for retrieval in rag architecture
The paragraph focuses on how content is vectorized in chunks like paragraphs to enable quick retrieval of the most relevant pieces for a given user question. It explains how vectors represent the essence of chunks numerically, with similar chunks having similar vector representations that allows matching user questions to related content.
Mindmap
Keywords
💡language model
💡prompt
💡vector
💡retrieve
💡augment
💡generation
💡vector database
💡prompt engineering
💡rag architecture
💡content ingestion
Highlights
Explains the retrieval augmented generation (RAG) architecture which leverages large language models on custom content
RAG systems can answer questions or generate content using a company's custom documents instead of just generic internet content
The prompt engineering before the actual question prompt allows RAG systems to guide the language model's response
Relevant custom content chunks are embedded into numeric vectors, allowing matches against the user's question vector
The top content chunks matching the question vector are added to the prompt to augment the language model's generation
Retrieval refers to finding relevant custom content chunks and augmentation refers to enhancing the language model's generation
RAG is a popular solution pattern for creating chatbot-like experiences from custom content with large language models
Example use cases include customer chatbots, product documentation answers, and internal ticket resolution suggestions
The prompt engineering provides instructions to the language model like branding guidelines or tone of voice
The retrieved content chunks become the prompt before the actual user question prompt
Paragraph chunks allow breaking down large content sources into smaller pieces for matching
Similar paragraph chunks have similar numeric vector representations for easily finding relevant matches
The user's question is also vectorized to find the top matching content vector chunks
Using the most relevant custom content with the question allows better responses than just a generic language model
The whole RAG architecture greatly enhances the user experience over just providing raw content results
Transcripts
hello everyone uh welcome to my code
deare uh video series um what I'm doing
is I'm rotating through three different
types of topics educational topics uh
use case topics and then kind of bias
ethics safety uh topic so now on the
education rotation and today what I
wanted to talk about is uh what is
retrieval augmented generation or rag uh
and you may think that I'm going into
some kind of nook and cranny of the AI
uh field but this is a very important
and popular kind of solution pattern
that I see um being used over and over
and over again for uh how to leverage
large language models so I thought I
would explain it uh to you uh and the
the the thing that this is used for is
basically systems that leverage large
language models but on your own content
so let me describe that if you think of
like the chat GPT experience and if you
think about that um relative to like the
search engine experience that we had
before if you ask a question like um I
don't know what color is the sky or how
do I fix this plumbing issue or
something like that a search engine
would go out uh or appear to go out
search the internet find relevant
content and then just list that content
for you list those links and then you as
a user would need to click on the links
that seem seem right read it digest it
and figure out the answer to your
question what a large language model
does is it seems to do that first part
meaning leverage the content on the
whole internet but instead of just
listing that content it sort of digests
it digests it combines it assembles it
together and answers your question sort
of generates an answer um so it's a
whole lot better I mean search engines
have been great but this is taking the
whole experience to another level and in
addition the question and answering uh
you can also give it instructions like
write me this document or write me a
lesson plan to teach geometry to seventh
graders uh and it will do something
similar it will kind of assemble content
that it SE that it has seen uh that
talks about geometry or seventh graders
or how to do lesson plans or whatever uh
pulls that together assembles it and
then writes out a lesson plan okay so
it's a much better experience than just
taking the raw content from the internet
but it really uh creates something new
from that now let's say you want that
same experience but on your own content
so it might be a chatbot on your website
or you might have a library of PDF
documents that this documentation for
one of your products uh and instead of
just linking the user to parag sections
of the documentation you want to
actually answer their question uh it
might be your service ticketing uh
system so when a new issue comes in you
could say how would I resolve this issue
and it can assemble past similar issues
uh and then come up with a new uh new
solution based on that so this is an
incredible experience that these large
language models offer but how can you
create that experience on your own
content uh that might not be available
to the internet or available to these
large language models well the solution
to this is this rag um architecture this
retrieval augmented uh generation
architecture so now I'm going to do my
best to explain that uh to
you so let's say you have a um
user and I'm going to use the example of
a uh patient chatbot and the content
source is going to be that content from
your website let's say or could be
content from PDF documents or or
whatever but you want this to be the
content to answer the patient's
questions so if the patient has a
question like how do I prepare for my
knee surgery instead of just going to
chat sheet PT and getting a generic
answer you'd like to provide an answer
that's from your health system or a
question like do you have parking you'd
like to provide an answer for your
health system for your the office where
the patient is seen okay so that's a
scenario that I'd like to do so the
patient has a
question uh and I'm going to do do you
have
parking have
parking um you can uh imagine that
question being bundled up into a prompt
what's called a prompt and I'll describe
this more
later so there is the question that
prompt is sent to a large language
model and that large language model will
come up with a response to that question
okay now um if you just wanted to use uh
chat GPT let's say or some other llm uh
without any extra content you could just
use this flow how do I prepare for my
knee surgery or do you have parking put
that into a prompt send that to the uh
large language model and get a response
back okay but uh but what we want to do
is enhance this experience with our own
content so let's say here is your
content
source and again this might be all the
content of your website or PDF documents
or internal ticketing system or
databases or that uh that sort of thing
and what you'd like to do is something
called called the prop before the propt
so in these systems you don't just send
the user question to the large language
model you usually have some level of
instructions So the instructions might
be you are a contact center specialist
working for a hospital answering patient
questions that come in over the Internet
uh please be uh nice to the patients and
responsive and folksy because that fits
with our brand or some instructions like
that are sometimes sent with the prompt
um and then uh
Additionally you want to provide the
information that the L llm needs to
answer the question so what you'd
ideally like is information from your
website to be included here um and uh
and that to be sent to the llm as well
so the full prompt might be your
instructions it might be something like
please use this content um in order to
answer the patient question at the end
and then you put in a bunch of
information about parking or about knee
surgery or whatever the patient asked
you put that in the prompt before the
prompt then you have the question then
you send that whole package to the llm
and the llm will give a great response
based on your
content okay with me so far so um so
this notion is the prop before the
prompt um and and that's why prompt
engineering and these types of things
are a big field right now now because
you can really hone the um these systems
by doing a better and better job with
the actual prompt before the prompt um
in uh in this
style now the last trick here is your
website or your content is huge and it
talks about all kinds of topics Beyond
parking and Beyond knee surgery so you
really want to somehow pull out only the
parts of your content that are relevant
to the patient's question so this is
another um a tricky part of this whole
rag architecture uh and the way that
works is that um you take all your
content and you break it into chunks or
these systems will break it into chunks
so chunk might be a paragraph of content
or a p or a couple paragraphs a page
something like that and then those um
chunks are sent to a large language
model could be the same one or a
different one and they are turned into a
vector
and uh so each each paragraph or each
chunk will have a
vector which is just is just a series of
numbers and that series of
numbers you can think of it as the
numeric representation of the essence of
that
paragraph and what's uh different about
these numbers just they're not random
numbers but paragraphs that talk about a
similar topic have close by numbers they
almost have the same vectors okay so in
addition to the uh it's a numera Zed
version of the paragraph but it's such
that similar paragraphs on similar
topics will have similar vectors will
have similar numbers so that means that
what happens is when um uh a user will
ask a question like do you have parking
let's
say then that is also sent to the llm in
real time right after the user asked the
question
that comes up with the vector as well
you could think of that as the question
vector and then what happens we do we do
a mathematical comparison real quick
between the vector of the question and
then the vectors of your content and
pick like the top five documents that
are closest to this question so do you
have parking will be a vector then you
have all your content and it's going to
try and find the five documents that
taught the most about parking basically
um and so it'll find those I don't know
what that is it'll find those documents
let's say uh from these it'll grab the
paragraphs associated with those
documents um and it'll use that
here so those will be the subset of your
content basically that is used as part
of the prompt before the prompt okay so
this whole uh concept is uh kind of
vectorizing your content uh typically
that then our storage in something
called a vector database which is
basically a representation of your
content in this numeric form and then
this system that you build this rag
system will uh take the question find
retrieve the most relevant content make
that as part of the prompt before the
prompt send that to the llm and then
you'll get a good response back actually
so it's a little bit confusing but um
but it's actually not that confusing um
uh I just made it more confusing by this
horrible uh horrible drawing but this
whole thing is um what is uh called rag
retrieval so you're retrieving the
relevant documents from your content
you're augmenting the generation process
so you're augmenting the lm's ability to
do generative AI based on the documents
that you retrieve so that's why it's
retrieval augmenting
generation okay so I hope that made
sense uh like I said this is a very
popular um solution pattern that I'm
seeing over and over again in fact the
majority of llm projects that I see are
this kind of thing using my content
packaging that up with an llm system to
create a kind of chat chpt like
experience for my employees or for my
customers for my users that kind of
thing and it works extremely well that's
why uh that's why it's so popular so I
hope that was interesting and
educational and made sense if you have
any questions please leave them for me
uh as part of the comments uh thank you
very much
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