Why Everyone is Freaking Out About RAG

Summary

The 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 It addresses issues like outdated data and lack of source transparency in LMs.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• 📚 Current LLMs often provide outdated answers or lack transparency in how they derive their answers, which can lead to misinformation.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LLM to a data store, allowing it to retrieve up-to-date information to generate responses, thus solving the problem of outdated data.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

00:00

🤖 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.

05:00

🔍 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.

Keywords

💡RAG

RAG stands for Retrieval-Augmented Generation. It is a technique that enhances the usability of Large Language Models (LLMs) by connecting them to a data store, allowing them to retrieve up-to-date information to generate more accurate responses. In the video, RAG is presented as a solution to the problem of outdated information provided by LLMs, as it enables them to access current data rather than relying solely on their training data.

💡LLMs

LLMs, or Large Language Models, are AI systems trained on vast amounts of text data to generate human-like responses. The video discusses the limitations of LLMs, such as providing outdated or incorrect information due to their reliance on training data that may not be current. RAG is introduced as a method to improve the accuracy and relevance of LLMs by integrating real-time data retrieval.

💡Data Store

A data store in the context of the video refers to a repository or database that contains up-to-date information. RAG utilizes a data store to fetch current and relevant data, which is then used by the LLM to generate responses. The video gives an example of connecting an LLM to a real-time database of NFL scores to ensure the information provided is current.

💡Retrieval

Retrieval in the video refers to the process by which an LLM, through RAG, accesses a data store to obtain specific pieces of information. This is a key component of RAG, as it allows the LLM to incorporate the most recent data into its responses, overcoming the limitation of relying on potentially outdated training data.

💡Out-of-Date Data

Out-of-date data is information that is no longer current or accurate due to the passage of time or new developments. The video highlights this as a major issue with existing LLMs, which may provide answers based on outdated training data. RAG addresses this problem by allowing LLMs to access and use the most recent data from a data store.

💡Source

In the video, 'source' refers to the origin of the information provided by the LLM. One of the benefits of RAG is that it allows developers and users to know the source of the data used by the LLM, enabling them to verify the accuracy of the information. This transparency is crucial for building trust in the responses generated by LLMs.

💡Reasoning Ability

Reasoning ability is the capacity of an LLM to understand and process information logically. The video emphasizes that by using RAG, LLMs can leverage their reasoning abilities to provide natural and contextually appropriate responses, rather than simply recalling information from their training data.

💡Vectorization

Vectorization in the context of the video is the process of converting text into numerical vectors that can be understood and processed by a computer system. This is a step in the RAG process, where the prompt is vectorized to facilitate the retrieval of relevant data from the data store based on similarity in vector representation.

💡HubSpot

HubSpot is mentioned in the video as the sponsor that provides a free resource on how to use Chat GPT at work. This resource is intended to help users understand and effectively utilize Chat GPT, an example of an LLM, in various applications. The video suggests that knowing how to use such tools is valuable, especially in the programming industry.

💡Choose Your Own Adventure Game

The video mentions a 'Choose Your Own Adventure' game as an example of an application that uses the RAG framework. This game demonstrates how RAG can be implemented in a practical and interactive way, allowing users to see the technique in action and understand its potential applications.

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]

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