Introduction
AI is everywhere.
It is hard not to interact at least once a day with a Large Language Model (LLM). The chatbots are here to stay. They’re in your apps, they help you write better, they compose emails, they read emails…well, they do a lot.
And I don’t think that that is bad. In fact, my opinion is the other way – at least so far. I defend and advocate for the use of AI in our daily lives because, let’s agree, it makes everything much easier.
I don’t have to spend time double-reading a document to find punctuation problems or type. AI does that for me. I don’t waste time writing that follow-up email every single Monday. AI does that for me. I don’t need to read a huge and boring contract when I have an AI to summarize the main takeaways and action points to me!
These are only some of AI’s great uses. If you’d like to know more use cases of LLMs to make our lives easier, I wrote a whole book about them.
Now, thinking as a data scientist and looking at the technical side, not everything is that bright and shiny.
LLMs are great for several general use cases that apply to anyone or any company. For example, coding, summarizing, or answering questions about general content created until the training cutoff date. However, when it comes to specific business applications, for a single purpose, or something new that didn’t make the cutoff date, that is when the models won’t be that useful if used out-of-the-box – meaning, they will not know the answer. Thus, it will need adjustments.
Training an LLM model can take months and millions of dollars. What is even worse is that if we don’t adjust and tune the model to our purpose, there will be unsatisfactory results or hallucinations (when the model’s response doesn’t make sense given our query).
So what is the solution, then? Spending a lot of money retraining the model to include our data?
Not really. That’s when the Retrieval-Augmented Generation (RAG) becomes useful.
RAG is a framework that combines getting information from an external knowledge base with large language models (LLMs). It helps AI models produce more accurate and relevant responses.
Let’s learn more about RAG next.
What is RAG?
Let me tell you a story to illustrate the concept.
I love movies. For some time in the past, I knew which movies were competing for the best movie category at the Oscars or the best actors and actresses. And I would certainly know which ones got the statue for that year. But now I am all rusty on that subject. If you asked me who was competing, I would not know. And even if I tried to answer you, I would give you a weak response.
So, to provide you with a quality response, I will do what everybody else does: search for the information online, obtain it, and then give it to you. What I just did is the same idea as the RAG: I obtained data from an external database to give you an answer.
When we enhance the LLM with a content store where it can go and retrieve data to augment (increase) its knowledge base, that is the RAG framework in action.
RAG is like creating a content store where the model can enhance its knowledge and respond more accurately.
AI is everywhere.
It is hard not to interact at least once a day with a Large Language Model (LLM). The chatbots are here to stay. They’re in your apps, they help you write better, they compose emails, they read emails…well, they do a lot.
And I don’t think that that is bad. In fact, my opinion is the other way – at least so far. I defend and advocate for the use of AI in our daily lives because, let’s agree, it makes everything much easier.
I don’t have to spend time double-reading a document to find punctuation problems or type. AI does that for me. I don’t waste time writing that follow-up email every single Monday. AI does that for me. I don’t need to read a huge and boring contract when I have an AI to summarize the main takeaways and action points to me!
These are only some of AI’s great uses. If you’d like to know more use cases of LLMs to make our lives easier, I wrote a whole book about them.
Now, thinking as a data scientist and looking at the technical side, not everything is that bright and shiny.
LLMs are great for several general use cases that apply to anyone or any company. For example, coding, summarizing, or answering questions about general content created until the training cutoff date. However, when it comes to specific business applications, for a single purpose, or something new that didn’t make the cutoff date, that is when the models won’t be that useful if used out-of-the-box – meaning, they will not know the answer. Thus, it will need adjustments.
Training an LLM model can take months and millions of dollars. What is even worse is that if we don’t adjust and tune the model to our purpose, there will be unsatisfactory results or hallucinations (when the model’s response doesn’t make sense given our query).
So what is the solution, then? Spending a lot of money retraining the model to include our data?
Not really. That’s when the Retrieval-Augmented Generation (RAG) becomes useful.
RAG is a framework that combines getting information from an external knowledge base with large language models (LLMs). It helps AI models produce more accurate and relevant responses.
Let’s learn more about RAG next.
What is RAG?
Let me tell you a story to illustrate the concept.
I love movies. For some time in the past, I knew which movies were competing for the best movie category at the Oscars or the best actors and actresses. And I would certainly know which ones got the statue for that year. But now I am all rusty on that subject. If you asked me who was competing, I would not know. And even if I tried to answer you, I would give you a weak response.
So, to provide you with a quality response, I will do what everybody else does: search for the information online, obtain it, and then give it to you. What I just did is the same idea as the RAG: I obtained data from an external database to give you an answer.
When we enhance the LLM with a content store where it can go and retrieve data to augment (increase) its knowledge base, that is the RAG framework in action.
RAG is like creating a content store where the model can enhance its knowledge and respond more accurately.
User prompt about Content C. LLM retrieves external content to aggregate to the answer. Image by the author.
Summarizing:Uses search algorithms to query external data sources, such as databases, knowledge bases, and web pages.
Pre-processes the retrieved information.
Incorporates the pre-processed information into the LLM.
Why use RAG?
Now that we know what the RAG framework is let’s understand why we should be using it.
Here are some of the benefits:Enhances factual accuracy by referencing real data.
RAG can help LLMs process and consolidate knowledge to create more relevant answers
RAG can help LLMs access additional knowledge bases, such as internal organizational data
RAG can help LLMs create more accurate domain-specific content
RAG can help reduce knowledge gaps and AI hallucination
As previously explained, I like to say that with the RAG framework, we are giving an internal search engine for the content we want it to add to the knowledge base.
Well. All of that is very interesting. But let’s see an application of RAG. We will learn how to create an AI-powered PDF Reader Assistant.
Project
This is an application that allows users to upload a PDF document and ask questions about its content using AI-powered natural language processing (NLP) tools. The app uses Streamlit as the front end.
Langchain, OpenAI’s GPT-4 model, and FAISS (Facebook AI Similarity Search) for document retrieval and question answering in the backend.
Let’s break down the steps for better understanding:Loading a PDF file and splitting it into chunks of text.This makes the data optimized for retrieval
Present the chunks to an embedding tool.Embeddings are numerical vector representations of data used to capture relationships, similarities, and meanings in a way that machines can understand. They are widely used in Natural Language Processing (NLP), recommender systems, and search engines.
Next, we put those chunks of text and embeddings in the same DB for retrieval.
Finally, we make it available to the LLM.
Data preparation
Preparing a content store for the LLM will take some steps, as we just saw. So, let’s start by creating a function that can load a file and split it into text chunks for efficient retrieval.
Summarizing:Uses search algorithms to query external data sources, such as databases, knowledge bases, and web pages.
Pre-processes the retrieved information.
Incorporates the pre-processed information into the LLM.
Why use RAG?
Now that we know what the RAG framework is let’s understand why we should be using it.
Here are some of the benefits:Enhances factual accuracy by referencing real data.
RAG can help LLMs process and consolidate knowledge to create more relevant answers
RAG can help LLMs access additional knowledge bases, such as internal organizational data
RAG can help LLMs create more accurate domain-specific content
RAG can help reduce knowledge gaps and AI hallucination
As previously explained, I like to say that with the RAG framework, we are giving an internal search engine for the content we want it to add to the knowledge base.
Well. All of that is very interesting. But let’s see an application of RAG. We will learn how to create an AI-powered PDF Reader Assistant.
Project
This is an application that allows users to upload a PDF document and ask questions about its content using AI-powered natural language processing (NLP) tools. The app uses Streamlit as the front end.
Langchain, OpenAI’s GPT-4 model, and FAISS (Facebook AI Similarity Search) for document retrieval and question answering in the backend.
Let’s break down the steps for better understanding:Loading a PDF file and splitting it into chunks of text.This makes the data optimized for retrieval
Present the chunks to an embedding tool.Embeddings are numerical vector representations of data used to capture relationships, similarities, and meanings in a way that machines can understand. They are widely used in Natural Language Processing (NLP), recommender systems, and search engines.
Next, we put those chunks of text and embeddings in the same DB for retrieval.
Finally, we make it available to the LLM.
Data preparation
Preparing a content store for the LLM will take some steps, as we just saw. So, let’s start by creating a function that can load a file and split it into text chunks for efficient retrieval.
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