Why Everyone is Freaking Out About RAG
Summary
TLDRThe video script introduces Retrieval Augmented Generation (RAG), a technique that enhances Language Models (LMs) by connecting them to a data store for up-to-date, accurate responses. It addresses issues like outdated data and lack of source transparency in LMs. RAG allows developers to use LMs for reasoning without relying on their training data, ensuring responses are current and verifiable. The script outlines RAG's architecture, benefits like avoiding retraining and providing sources, and concerns about data relevance and retrieval efficiency.
Takeaways
- 🤖 RAG stands for Retrieval-Augmented Generation, a technique that enhances the usability of Large Language Models (LLMs) by addressing their limitations.
- 📚 Current LLMs often provide outdated answers or lack transparency in how they derive their answers, which can lead to misinformation.
- 🔍 RAG connects an LLM to a data store, allowing it to retrieve up-to-date information to generate responses, thus solving the problem of outdated data.
- 💡 By using RAG, developers can implement LLMs in their applications with confidence in the accuracy of the results and the ability to trace the source of information.
- 📈 RAG enables LLMs to use their reasoning abilities rather than relying on their training data, making them more effective for natural language understanding and generation.
- 🛠️ The architecture of RAG involves vectorizing prompts, using a retriever to find relevant data, and then augmenting the LLM with this data to provide evidence-based answers.
- 🚫 One of the benefits of RAG is that it allows developers to avoid retraining LLMs, instead keeping the data source updated to ensure current information.
- 🔗 RAG provides a source for data, allowing users to validate the information and know where the LLM derived its answers from, increasing trust in the model's responses.
- 🛑 RAG also allows LLMs to admit when they don't have an answer, avoiding the provision of misleading or incorrect information.
- 👀 Concerns with RAG include the need for efficient and accurate retrieval of relevant information, and ensuring the model uses this data correctly without introducing latency.
- 📘 Understanding RAG requires knowledge of how LLMs work, and resources like HubSpot's 'How to Use Chat GPT at Work' can provide valuable insights for developers.
Q & A
What does RAG stand for and what is its purpose?
-RAG stands for Retrieval-Augmented Generation. Its purpose is to enhance the usability of Large Language Models (LLMs) by addressing issues such as outdated information and the lack of transparency in how the models generate answers.
What are the two major issues with current LLMs as mentioned in the script?
-The two major issues with current LLMs are outdated data and the lack of a source for the information provided. LLMs may give answers based on data they were trained on, which may not be current, and they often do not provide a way to verify the accuracy of their answers.
How does RAG address the problem of outdated information in LLMs?
-RAG addresses the problem of outdated information by connecting an LLM to a data store. When the LLM needs to generate an answer, it retrieves up-to-date data from the data store and uses this data to inform its response, ensuring the information is current.
How does RAG provide transparency in the information provided by an LLM?
-RAG provides transparency by allowing the LLM to retrieve data from a specific source, which can then be used to generate a response. This means that users can trace back the information to its original source, verifying the accuracy and relevance of the data.
What is an example of how RAG can be used in a practical scenario?
-An example given in the script is using an LLM to retrieve up-to-date scores for a football game. The LLM would be connected to a real-time database containing NFL scores, and it would retrieve the relevant information from this database to answer questions about the game scores.
How does RAG change the way we use LLMs?
-RAG changes the way we use LLMs by shifting from using them as knowledge bases to using them as reasoning tools that understand natural language. The LLMs are augmented with up-to-date data from a controlled data source, allowing them to provide more accurate and relevant responses.
What are some benefits of using RAG with LLMs?
-Some benefits include avoiding the need to retrain language models with updated data, providing a source for the information so it can be validated, and allowing the model to accurately state when it does not have an answer based on the provided data.
What are some concerns or challenges with implementing RAG?
-Concerns with implementing RAG include ensuring that the data augmented into the model is good and relevant, developing an efficient and accurate way to retrieve relevant information quickly to avoid latency, and making sure the model uses the augmented data correctly.
How does RAG handle situations where the data source does not have an answer?
-RAG allows the LLM to state that it does not have an answer based on the provided data, rather than giving a potentially incorrect or misleading answer.
What is the role of a vector database in the RAG process?
-A vector database is used to vectorize the prompt and find documents with similar vector representations, which are then returned as relevant data for the LLM to use in generating a response.
How can developers learn more about using RAG and LLMs effectively?
-Developers can learn more about using RAG and LLMs effectively through resources like the free guide 'How to Use Chat GPT at Work' provided by HubSpot, which includes expert insights and practical applications.
Outlines
🤖 Introduction to Retrieval Augmented Generation (RAG)
The script introduces the concept of Retrieval Augmented Generation (RAG), a technique designed to enhance the usability of Large Language Models (LLMs). The narrator discusses the limitations of current LLMs, such as providing outdated answers and lacking transparency in the source of information. RAG addresses these issues by connecting an LLM to a data store, allowing it to retrieve and incorporate up-to-date information into its responses. The video promises to break down everything one needs to know about RAG, including its benefits and how it works, with a simple example of using an LLM to retrieve real-time NFL scores from a database.
🔍 High-Level Architecture and Benefits of RAG
This paragraph delves into the high-level architecture of RAG, starting with the generation of a prompt, which is then vectorized to retrieve relevant data. The retrieved data is used to augment the LLM, providing a reply and evidence from the data source. The benefits of using RAG include avoiding the need to retrain language models by simply keeping the data source up to date, knowing the source of data for validation, and the model's ability to admit when it lacks the answer. The narrator also mentions potential concerns, such as the necessity for good and relevant data augmentation, efficient retrieval methods, and ensuring the model uses the augmented data accurately. The video includes a resource from HubSpot for further understanding of LLMs and a mention of a related video on creating a 'Choose Your Own Adventure' game using RAG.
Mindmap
Keywords
💡RAG
💡LLMs
💡Data Store
💡Retrieval
💡Out-of-Date Data
💡Source
💡Reasoning Ability
💡Vectorization
💡HubSpot
💡Choose Your Own Adventure Game
Highlights
RAG stands for Retrieval-Augmented Generation, a technique that enhances the usability of Large Language Models (LLMs).
LLMs often provide outdated answers due to reliance on training data without access to new information.
RAG addresses issues of accuracy and source transparency by connecting an LLM to a data store for up-to-date information.
An example of RAG in action is using an LLM to retrieve real-time NFL scores from a database.
RAG solves the problem of outdated data and lack of source information by retrieving data from a data store.
LLMs are used for reasoning and natural language understanding, not as a knowledge base, in RAG.
RAG allows developers to control the data injected into the LLM, ensuring accuracy and up-to-date information.
HubSpot offers a free resource on how to use Chat GPT effectively in the workplace.
The architecture of RAG involves a prompt, vectorization, retrieval, augmentation, and evidence provision.
RAG provides the benefit of avoiding retraining of language models by keeping the data source up to date.
Developers gain the advantage of knowing the source of data and being able to validate it.
RAG enables the LLM to admit when it doesn't know an answer, avoiding misleading information.
The success of RAG depends on the quality and relevance of the augmented data.
Efficient and accurate retrieval of information based on prompts is crucial for RAG.
Vector databases can be used for efficient retrieval by vectorizing prompts and finding similar documents.
RAG requires careful instruction of the model to ensure it uses only the augmented data for responses.
The video provides a general understanding of RAG to encourage further exploration and application.
A demonstration of RAG is given through a 'Choose Your Own Adventure' game video.
Transcripts
what is Rag and what you need to know
about it as a developer well rag stands
for retrieval augmented generation and
this is something that promises to
drastically enhance the usability of
llms we've probably seen that llms have
quite a few issues they are very usable
and a massive tool but if you wanted to
implement them into your own application
you can't be confident that the results
that they're giving you are accurate and
you don't know how they came up with the
answer rag is something that helps with
that problem and I'm going to break down
everything you need to know about it in
this video
[Music]
so let's begin with the problems with
current llms these llms are giving you
answers that often times are out of date
the reason for that is they're only
pulling data from the data that they
were trained on whenever that date
occurred so in the case of Chad gbt
you've probably seen that famous reply
where it says I don't know that answer
because I've only been trained up to
September 2021 or in a worst case
scenario it gives you an answer but that
answer is actually incorrect because
since then then we've come to some new
discovery or found some new information
that makes that answer obsolete for
example if you're asking things about
scientific research it'll actually give
you an answer most times but it might
not be the most upto-date or current
answer yet it's very convincing and if
you don't know the correct answer you
could be easily misled and end up using
inaccurate information so that's the
first major problem out ofd data next
problem is that a lot of times you don't
have any idea how the llm actually came
up with the answer it doesn't give you a
source you're kind of just blindly
trusting that what it gave you is
accurate and even if it is accurate a
lot of times you'd probably like to know
where it C that information from so you
could go and double check it yourself so
these are the two major issues outof
date and no source so how do we fix that
using retrieval augmented generation or
rack well rag is actually quite simple
what this does is connect an llm to a
data store this means that that when the
llm actually wants to come up with an
answer rather than just using its
training data it will actually go and
ask a retriever to get a specific piece
of data or some content from the data
store it will then inject that kind of
into the prompt or into the llm model
and then it will use that data that it
retrieved that likely is up to date to
generate a reply so to give you a super
simple example of this let's imagine I
want to use an llm to retrieve upto-date
scores for a football game now obviously
there's better ways to do this but let's
say for some reason I want to use an llm
well what I would do is connect that to
a realtime database that contains all of
the NFL scores that database we're going
to trust is always up toate and contains
the accurate information now what will
happen is when I have a prompt or I give
that to chat GPT or the llm whatever it
is it will now go to the data store it
will retrieve the relevant information
based on the data in my prompt it will
kind of inject that into the llm and
then it will use the data it just
retrieved to answer my question so now
we've kind of solved both problems I
have updated information and I have a
source for where that information is
coming from it's that data store so if I
want to know where I actually got my
data or I want to go there and double
check it I can simply go to the original
source and now what we're doing is we're
using the llm for its reasoning ability
and ability to understand natural
language not its huge memory from a
massive data or training set this is
really where this type of technique
comes in rather than using an llm as a
knowledge base you're using it as
something that can reason something that
can give you a natural reply and can
understand what you're asking better
than probably any model you could train
on your own so it's really the interface
for some type of application and then
you have the actual data that gets
injected and brought into the llm and
you can control that data you could have
data for I don't know your call center
you could have data for an application
or a stat tracking website whatever you
want any data you want you can augment
the llm with that and allow it to reason
solely based on that data and that way
you know you're getting accurate
upto-date information and if you want to
check that information you can go
directly to the source now just as a
quick note here before we dive in too
far to really understand the benefit of
something like rag you first need to
understand how llms actually work and
for most of you you're probably going to
be interacting with something like chat
gbt now fortunately our video sponsor
HubSpot actually has a completely free
resource called how to use chat gbt at
work that breaks down exactly how chat
gbd Works gives you expert insights and
tells you how you can use it to its full
ability now I put the link in the
description so you guys can check it out
but this resource is packed with
knowledge and it even has 100 actionable
prps that you can use today to really
leverage the full power of chat GPT
knowing how to use a tool like this
effectively is absolutely a game Cher
especially in the programming industry
and again you guys can check that out
from the link in the description a
massive thank you to HubSpot for making
this resource and tons of others
completely free
[Music]
so to give you the highlevel
architecture here we have a prompt this
is the first step once you generate the
prompt you can actually vectorize that
and you can then go to a retriever which
will go and find all of the relevant
data required for the specific prompt it
will then augment the llm with that so
it will essentially pass that into the
prompt the llm will give you some type
of reply and then it can actually
provide evidence directly from your data
source as to why it came up with that
answer now this also gives another
advantage that if your data source
doesn't have the answer the llm can tell
you that rather than giving you a
convincing answer that's actually wrong
or misleading it can simply say hey
based on what you gave me here for data
I don't actually have the answer now you
can decide whether that's better or
worse but in my opinion I'd rather the
llm tell me I don't know than tell me an
incorrect and false statement or kind of
false information that might mislead me
or have some pretty bad
repercussions
so now let's quickly dive into some of
the major benefits of using a technique
like rack first of all this allows you
to avoid retraining language models and
instead augmenting them with up-to-date
information we've already touched on
this but typically if you'd want the llm
to have upto-date data you'd have to
retrain it on that data or if you want
it to work specifically for your company
information you train it on that data
now you no longer need to do that you
simply keep your data source up to dat
and all of a sudden the model works
exactly as you would expect now the next
major benefit of course is the source
knowing where the data actually came
from and being able to validate that is
massive and then lastly kind of touching
on that being able to actually answer
and say I don't know is a huge benefit
so if the model doesn't know you can
then go to the data source add the
correct information or at least you know
you're not getting a misleading
answer now as well as all of these
benefits there are a few concerns that
you want to keep in mind first of all
all of this only works works if the data
that's augmented into the model is good
and relevant now that means you need to
come up with a quick efficient and
accurate way for retrieving relevant
information based on a prompt now
there's a lot of complex stuff you can
do here but the simplest way to do this
would be to use something like a vector
database that means you would vectorize
the prompt you would then go in the
vector database and you would find all
of the documents that have similarity
based on their Vector representation and
then return those but you can't simply
return every piece of information you
have to be selective at what you're
returning and this needs to happen very
very quickly it can't introduce a ton of
latency otherwise that's going to be a
poor user experience now at the same
time you want to make sure that the
model is accurately using this augmented
data so there's some special prompts and
ways that you can kind of instruct the
model to make sure it only gives you
information based on the data that was
augmented into it now just as a point of
clarity here by no means am I an expert
in rag I'm sure there might be some
slight inaccuracies in this video but I
wanted to share with you the the general
idea of rag so that you guys can go look
it up and see how you can use it in your
applications now I actually did make a
video that uses this type of technique
and framework to generate a Choose Your
Own Adventure game really interesting
and probably the simplest example to see
exactly how this works so if you want
that I'll put it in the description and
pop the video on the screen here anyways
if you guys enjoyed make sure you leave
a like subscribe to the channel and I
will see you in another
[Music]
one
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