RAG: The Smart Helper for Computer Programs

Discover how Retrieval-Augmented Generation (RAG) helps computer programs like GPT find accurate and up-to-date information, making them smarter and more helpful.

Hello! Today, I would like to explain a technology called Retrieval Augement Generation (RAG) in a simple and understandable way. With RAG, anyone can create their own chatbot even if they don't have expertise in artificial intelligence. What is RAG, and how does it work exactly? Let's explore it and how this technology is used to create better LLMs.

In today’s rundown:

🧑‍💻 Targeted audience: technologists, AI chatbot creators, and solo AI developers.
Problem statement: the drawbacks of LLMs.
What is RAG?
RAG process - a simple breakdown.

Read time: 3.5 minutes.

Problem statement

Training an AI model could be an expensive endeavor. OpenAI, as of January 27, 2024, has not updated its data beyond April 2023. In addition, though there are numerous AIs based on public data, none of them are trained on your specific data. Imagine you need to create an AI model that is trained on your data and tailored to solve your unique problems. Retraining a publicly available model is too costly, particularly for indie developers and solo creators. So, what is the solution?

What is RAG?

What is RAG in Simple Terms?: Imagine you're working on a big school project, and you need to find lots of information. You could read lots of books and websites, right? That's kind of what RAG does for computer programs that talk to us, called Large Language Models (like GPT). These programs are super smart, but sometimes, they need help finding the newest and most accurate info. RAG is like a helper that quickly finds the right information from books, websites, or other places so the computer program can give better answers.
Why is RAG Important? Consider when you ask a question about a smart device or computer program. You want the answer to be right and up-to-date. Without RAG, these programs might make mistakes or use old information. But with RAG, they can look up the latest information and be more accurate, just like how you would do research for your school project.
How Does RAG Work? Here's a simple way to understand it:
1. Question Time: First, you ask the computer program a question, like "Who won the soccer match yesterday?"
2. The Helper Looks for Answers: RAG starts looking for the answer in different places - maybe news websites or recent articles.
3. Bringing it All Together: Once RAG finds the right information, it helps the computer program understand it and give you a good answer.
4. Answer Time: Finally, the computer program tells you the answer, like "Team A won the match 2-1 yesterday!"
What Makes RAG Special?
1. Stays Up to Date: RAG keeps the computer program informed with the latest news or facts.
2. More Accurate Answers: Because RAG finds the most recent information, the computer program's answers are more likely to be correct.
3. Saves Time and Effort: Just like how it's easier to ask someone for an answer instead of looking it up yourself, RAG makes it easier for the computer program to get information without needing to be updated all the time.

RAG - a simple breakdown

Custom Knowledge Base

Custom Knowledge Base for RAG.

The starting point for any LLMs is data. The custom knowledge base is a library full of YOUR information, where RAG searches for data. It could be anything from documents to digital data in any type of format.

Chunking

Chunking-Breaking Down Information.

Chunking in Retrieval-Augmented Generation (RAG) is like dividing a big book into smaller chapters so it's easier to find exactly what you need. When RAG searches for information, it doesn't look at huge documents simultaneously. Instead, it breaks them down into smaller pieces or "chunks." This way, when you ask a question, RAG can quickly find and use just the relevant part of the information rather than going through everything. It's like having a really efficient way to skim through books to find exactly the right information for your question.

Embeddings

Embeddings in RAG.

Think of embeddings as a way to transform a story into a series of numbers that a computer can understand. The embedding model is the tool that does this. This process involves converting text into vectors using embedding language models. RAG uses these embeddings to search and retrieve information from various sources such as databases, document repositories, or APIs effectively. Once the relevant information is found, it's combined with the user's initial query to enhance the response generated by the large language model. This approach not only improves the accuracy and relevance of the model's responses but also allows the system to remain up-to-date with current information.

Vector Database

Vector Database For RAG.

Vector database stores the numerical representations (or vectors) of text data, which are created through the embedding process. When RAG processes a user's query, this database is used to quickly and efficiently search for the most relevant pieces of information. By comparing the vector representation of the user's query to those in the database, RAG identifies and retrieves the most pertinent information. This process ensures that the responses generated by the Large Language Model are both relevant and informed by the latest, most accurate data available.

Prompt template

The prompt template in Retrieval-Augmented Generation (RAG) acts as a guide or blueprint for generating responses. Imagine you're writing a letter; a prompt template is like a sample letter that helps RAG understand how to craft a reply. It combines your question with information retrieved from its extensive library to provide the most accurate response possible. This template ensures that the responses are not only relevant to the query but also structured in a coherent and understandable manner. Essentially, the prompt template is a critical component in directing how RAG processes and integrates the user's query with the retrieved data to formulate a well-structured and informative answer.

The toolset that enables RAG

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That’s a wrap! 🌯

RAG is a super-helpful library assistant for computer programs. It helps them find the best, most recent information so they can give better answers to our questions! RAG can be a buzzword for some people, so hopefully, this article will help explain its role and how simple it is to use for AI models.

Thank you for reading ❤️I hope you will find these insights helpful. Please contact us at [email protected] with suggestions, feedback, or anything!