RAG Explained

Summary

The video script explores the concept of Retrieval Augmented Generation (RAG), drawing an analogy between a journalist seeking information from a librarian and a large language model (LLM) querying a vector database for relevant data. It discusses the importance of clean, governed data and transparent LLMs to ensure accurate and reliable outputs, especially in business-critical applications. The conversation highlights the need for trust in data, akin to a journalist's trust in a librarian's expertise, to mitigate concerns about inaccuracies and biases in AI-generated responses.

Takeaways

• 📚 The analogy of a journalist and a librarian is used to explain the concept of Retrieval Augmented Generation (RAG), where a large language model (LLM) retrieves relevant data from a vector database to answer specific questions.
• 🤖 The user, in this case a business analyst, poses a question that requires specific and up-to-date information, which an LLM alone might not have.
• 🔍 To answer specific and dynamic questions, multiple data sources may be needed, which are then aggregated into a vector database for the LLM to access.
• 📊 A vector database represents structured and unstructured data in a mathematical form, making it easier for machine learning models to understand and use.
• 🔗 The data retrieved from the vector database is embedded into the original prompt, which is then processed by the LLM to generate an accurate and data-backed response.
• 🆕 As new data is added to the vector database, the embeddings are updated, ensuring that subsequent queries are answered with the most current information.
• 🚫 Enterprises are concerned about deploying RAG in customer-facing applications due to the risk of hallucinations, inaccuracies, or perpetuation of biases.
• 🧹 Ensuring data cleanliness, governance, and management is crucial to mitigate concerns about the quality and reliability of the data fed into the vector database.
• 🔍 Transparency in how LLMs are trained is important for businesses to trust the technology and ensure that there are no inaccuracies or biases in the training data.
• 🏢 For businesses, it's critical to maintain brand reputation by ensuring that the LLMs used provide accurate and reliable answers to their specific questions.

Q & A

What is the role of a librarian in the context of the journalist's research?

In the context of the journalist's research, the librarian acts as an expert who helps the journalist find relevant books on specific topics by querying the library's collection.

How does the librarian's role relate to Retrieval Augmented Generation (RAG) in AI?

The librarian's role in providing relevant books to the journalist is analogous to RAG, where large language models use vector databases to retrieve key sources of data and information to answer a question.

What is the significance of the business analyst's question about revenue in Q1 from the northeast region?

The business analyst's question is significant as it represents a specific and time-sensitive query that requires up-to-date and accurate data, highlighting the need for a reliable data retrieval system.

Why is it important to separate general language understanding from specific business knowledge in LLMs?

It is important to separate general language understanding from specific business knowledge in LLMs because the latter is unique to each business and changes over time, necessitating tailored data sources for accurate responses.

What is a vector database and how does it relate to answering specific questions?

A vector database is a mathematical representation of structured and unstructured data that can be queried to retrieve relevant data for answering specific questions, enhancing the capabilities of LLMs.

How does the inclusion of vector embeddings in prompts affect the output of LLMs?

Including vector embeddings in prompts provides LLMs with the relevant, up-to-date data they need to generate accurate and informed responses to specific questions.

Why is it crucial for the data in the vector database to be updated as new information becomes available?

Updating the data in the vector database as new information becomes available ensures that the LLMs have access to the most current data, which is essential for providing accurate and relevant answers to questions.

What are some concerns enterprises have about deploying AI technologies in business-critical applications?

Enterprises are concerned about the potential for AI technologies to produce hallucinations, inaccuracies, or perpetuate bias in customer-facing, business-critical applications.

How can enterprises mitigate concerns about the accuracy and bias of AI-generated outputs?

Enterprises can mitigate concerns by ensuring good data governance, proper data management, and using transparent LLMs with clear training data to avoid inaccuracies and biases.

Why is transparency in the training of LLMs important for businesses?

Transparency in the training of LLMs is important for businesses to ensure that the models do not contain inaccuracies, intellectual property issues, or data that could lead to biased outputs, thus protecting the brand's reputation.

How does the analogy of the journalist and librarian relate to the trust in data for AI use cases in business?

The analogy of the journalist and librarian highlights the importance of trust in data for AI use cases in business, emphasizing the need for confidence in the accuracy and reliability of the data used by LLMs.

Outlines

00:00

📚 Retrieval Augmented Generation (RAG) Explained

This paragraph introduces the concept of Retrieval Augmented Generation (RAG) by drawing a parallel with a journalist seeking information from a librarian. The journalist, despite having a general idea, needs specific data for an article, which the librarian provides by fetching relevant books. This is likened to how large language models (LLMs) query vector databases to retrieve pertinent data for answering questions. The conversation then shifts to a business context, where a business analyst might ask about revenue from a specific region. The paragraph highlights the need for multiple data sources to answer specific, time-sensitive questions and introduces the concept of vector databases as a means to store and retrieve structured and unstructured data in a format that is accessible to machine learning models.

05:00

🛡️ Addressing Concerns in Deploying AI in Business-Critical Applications

The second paragraph addresses concerns about deploying AI technologies in customer-facing, business-critical applications, such as the risk of producing inaccurate results or perpetuating bias. It emphasizes the importance of data quality, governance, and management to ensure the vector database contains accurate and up-to-date information. The discussion points out the need for transparency in the training of large language models to avoid inaccuracies and biases. The paragraph concludes by stressing the importance of trust in data, akin to the trust a journalist has in the library's resources, for successful implementation of generative AI in business settings.

Keywords

💡Journalist

A journalist is a person who collects, writes, or distributes news or other current information. In the context of the video, the journalist represents a user seeking information on a specific topic. The journalist's role is to gather and verify information, which parallels the process of a language model retrieving relevant data from a vector database to answer a query accurately.

💡Librarian

A librarian is an expert in managing and organizing information resources, such as books, in a library. In the video, the librarian is analogous to a system that retrieves relevant data for a language model. The librarian's expertise in locating books is compared to the system's ability to fetch the right data from a vector database, highlighting the importance of accurate and relevant information retrieval.

💡Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation is a machine learning technique where a large language model is augmented with information retrieved from a database to answer questions more accurately. The video uses RAG to illustrate how a language model can be enhanced by external data sources, like a librarian fetching books for a journalist, to provide comprehensive answers to specific queries.

💡Vector Database

A vector database is a system for storing and retrieving data based on vector representations of information. In the video, vector databases are used to store structured and unstructured data in a format that is easily accessible by machine learning models. The concept is central to the video's theme, as it explains how data is organized and made available for retrieval to answer specific questions, much like how a librarian organizes books in a library.

💡Language Model

A language model is a computational model that analyzes and predicts natural language data. In the video, language models are used to process and generate human-like text based on the data retrieved from a vector database. The model's ability to understand and respond to prompts is crucial for providing accurate and relevant information, as illustrated by the journalist's need for precise data.

💡Business Analyst

A business analyst is a professional who analyzes data and uses it to help businesses make decisions. In the video, the business analyst is used as an example of a user who might ask a specific question, such as revenue figures for a particular quarter and region. This role demonstrates the practical application of data retrieval and language models in a business context.

💡Data Governance

Data governance is the process of managing and controlling the availability, usability, integrity, and security of data. The video emphasizes the importance of data governance in ensuring that the data used by language models is clean, accurate, and up-to-date. Good data governance practices are compared to the librarian's role in maintaining the quality and relevance of the books in a library.

💡Data Management

Data management involves the planning, controlling, and providing access to the data an organization uses. In the video, data management is highlighted as a critical component in the process of using AI and language models. Effective data management ensures that the data fed into the vector database and, subsequently, the language model, is reliable and supports accurate outcomes.

💡Hallucinations

In the context of AI, 'hallucinations' refer to the generation of incorrect or nonsensical information by a model. The video discusses the concern that language models might produce hallucinations or inaccurate results if not properly trained or if the data they rely on is flawed. This term is used to illustrate the risks of using AI in business-critical applications without proper safeguards.

💡Bias

Bias in AI refers to the model's倾向 to favor certain outcomes over others due to skewed or unrepresentative training data. The video addresses the concern that AI models might perpetuate biases if not trained on diverse and balanced data. The concept is important in ensuring that AI-generated outputs are fair and do not discriminate against any group, aligning with the video's theme of responsible AI use.

Highlights

The analogy of a journalist and a librarian is used to explain the Retrieval Augmented Generation (RAG) process.

RAG involves large language models using vector databases to retrieve relevant information for answering questions.

The importance of the librarian's (or AI system's) role in providing accurate and up-to-date information is emphasized.

The concept of a business analyst querying for specific data, like revenue from a particular region, is introduced.

The necessity of understanding the difference between general and specific business-related questions for AI is discussed.

The use of multiple data sources to answer specific questions is highlighted.

Vector databases are introduced as a way to store structured and unstructured data in a format suitable for AI.

The process of querying a vector database to retrieve embeddings for specific prompts is explained.

The integration of retrieved data into a prompt for a large language model to generate an answer is described.

The dynamic updating of embeddings in the vector database as new data comes in is mentioned.

The challenge of deploying AI in customer-facing applications and the concerns about inaccuracies and biases are raised.

The importance of clean, governed, and managed data in the vector database for accurate AI outputs is stressed.

The need for transparency in how large language models are trained to avoid inaccuracies and biases is discussed.

The analogy of trust in the library's data is drawn to the need for confidence in AI-generated business data.

The significance of governance, AI, and data management in ensuring the best results from AI is concluded.

Transcripts

So imagine you're a journalist

and you want to write an article

on a specific topic.

Now you have a pretty good general idea about this topic,

but you'd like to do some more research.

So you go to your local library.

Now, this library has thousands of books

on multiple different topics.

But how do you know, as a journalist,

which books are relevant for your topic?

Well, you go to the librarian.

Now, the librarian is the expert on what books contain,

which information in the library.

So, our journalist queries the librarian to retrieve

books on certain topics.

And the librarian produces those books

and provides them back to the journalist.

Now, the librarian isn't the expert on writing the article,

and the journalist isn't the expert

on finding the most up-to-date and relevant information.

But with the combination of the two, we can get the job done.

Luv, this sounds like a lot like the process of RAG,

or Retrieval Augmented Generation,

where large language models call on vector databases

to provide key sources of data and information

to answer a question.

I'm not seeing the connection.

Can you help me understand a little bit better?

Sure.

So we have a user.

In your scenario, it's that journalist.

And they have a question.

So what types of questions would you want to ask?

Maybe we can make this more of a business context.

Yeah, so let's say this is a business analyst.

And let's say they want to ask,

"What was revenue in Q1 from customers in the northeast region?"

Right, so that's your prompt.

Okay, so a couple of questions on that user.

Does it have to be a person or could it be something else too.

Yeah. So this doesn't necessarily have to be a user.

It could be a bot

or it could be another application.

Even the question that we're talking about,

"What was our revenue in Q1 from the northeast?"

You know, the first part of that question,

it's pretty easy for, you know, a general LLM to understand, right?

What was our revenue?

But it's that second part in Q1 from customers in the northeast.

That's not something that LLMs are trained on, right.

It's very specific to our business and it changes over time.

So we have to treat those separately.

So how do we manage that part of the request?

Exactly.

You'll need multiple different sources of data potentially

to answer a specific question, right?

Whether that's maybe a PDF, or

another business application,

or maybe some images,

whatever that question is, we need the appropriate data

in order to provide the answer back.

What technology allows us to aggregate that data,

and use it for our LLM.

Yeah, so we can take this data

and we can put it into what we call a vector database.

a vector database is a mathematical representation

of structured and unstructured data

similar to what we might see in an array.

Gotcha, and these arrays are better suited or easier to understand

for machine learning or generative AI models

versus just that underlying unstructured data.

Exactly.

We query our vector database, right?

And we get back an embedding that includes

the relevant data for which we're prompting.

And then we include it back into the original prompt, right?

Yeah, exactly.

That feeds back into the prompt.

And then once we're at this point,

we move over to the other side of the equation,

which is a large language model.

Gotcha, so that that prompt,

that includes the vector embeddings now

are fed into the large language model,

which then produces the output

with the answer to our original question

with sourced, up-to-date and accurate data.

Exactly. And that's a crucial aspect of it.

As new data comes in to this vector database,

where things that are updated back to your relevant question

around performance in Q1,

as new data comes in, those embeddings are updated.

So when that question is asked the second time,

we have more relevant data in order to provide back

to the LLM who then generates the output in the answer.

OK, very cool.

So Shawn, this sounds a lot like my original analogy there

with the librarian and our journalist, right?

So the journalists trusts that the information in the library

is accurate and correct.

Now, one of the challenges that I see is when I'm talking to enterprise customers

is they're concerned about deploying this kind of technology

into customer facing, business critical applications.

So if they're building applications, taking customer orders,

processing refunds,

they're worried that these kinds of technologies

can produce hallucinations or inaccurate results, right?

Or perpetuate some kind of bias.

What are some things that can be done

to help mitigate some of these concerns?

That brings up a great point Luv.

Data that comes in on this side,

but also on this side,

is incredibly important to the output that we get

when we go to make that prompt and get that answer back.

So it really is true: "Garbage in and garbage out", right?

So we need to make sure we have good data that comes into the vector database.

We need to make sure that data is clean,

governed and managed properly.

Gotcha, so what I'm hearing is

that things like governance

and data management

are of course crucial to the vector database, right?

So making sure that the actual information that's flowing through into the model,

such as the business results in the sample prompt we talked about

is governance and clean,

but also crucially,

on the large language model side,

we need to make sure that we're not using a

large language model that takes a black box approach, right?

So, a model where you don't actually know

what is the underlying data that went into training it, right?

You don't know if there's any intellectual property in there.

You don't know if there's inaccuracies in there

or you don't know if there are, pieces of data

that will end up perpetuating bias in your output results.

Right?

So as a business,

and as a business that's trying to

manage and uphold their brand reputation,

it's absolutely critical to make sure that we're taking an approach

that uses LLMs that are transparent in how they were trained,

and we can be 100% certain

that there aren't any inaccuracies

or data that's not supposed to be in there, right?

Yeah, exactly.

It's incredibly important, especially as a brand, that we get the right answers.

We've seen the results of impact, and especially back

to our original question around "what was our revenue in Q1", right?

We don't want that to be impacted by the results of a question

that comes from, you know, that prompts one of our LLMs.

Exactly, exactly.

So very powerful technology.

But it makes me think back to the the library.

Our journalist and librarian, they both trust the data and the books that are in the library.

We have to have that same kind of confidence

when we're building out these types of generative AI use cases for business as well.

Exactly, Luv.

So governance, AI, but also data and data management

are incredibly important to this process.

We need all three in order to get the best result.