What is RAG (Retrieval Augmented Generation)?: Building Smarter AI Applications with Your Data

In today’s fast-moving digital age, generative AI is truly game-changing. Retrieval Augmented Generation, or RAG, is at the forefront of this innovation, making AI applications smarter by connecting advanced AI models with your source data to generate responses using this knowledge base. Now, the big question: how does it work? In this post, I’ll break down everything software engineers need to know to succeed with this cutting-edge technology. But first, let’s cover the basics.

RAG System Explained

Think about this: What if ChatGPT could read all your documents and answer questions about them? That’s exactly what retrieval augmented generation RAG does. RAG (Retrieval Augmented Generation) is a way to connect a large language model like ChatGPT to your information.

Here’s a simple example:

• Normal AI models: Can only use what they learned during training.
• AI models with RAG: Can read and understand your specific documents, files, and data.

It’s like giving the AI a personal library of your information to work with. When you ask a question, it searches through this library to give you accurate search results about your data.

Large Language Models (LLMs) Limitations

Large Language Models (LLMs) like GPT, Gemini, and Claude are revolutionizing the way we communicate and access information, but they have some critical limitations:

• They only know what they learned during training.
• They don’t have access to events that happened after their training period.

For example, if you ask ChatGPT about your company’s latest product, it won’t know anything about it unless it was released before its training data cutoff.

In these situations, the AI language model might make up information. For instance, it might say your company has product X, even if X never existed. This is called hallucination. It’s like when someone tries to answer a question they don’t fully understand—they might mix up facts or create false information.

This is why we need a better solution—a way to give AI models access to accurate, up-to-date, and more detailed information. This is where RAG comes in.

Unlike fine-tuning, which hardwires knowledge into the model, RAG allows for dynamic updates with real-time data.

How Retrieval Augmented Generation (RAG) Helps

RAG solves these problems by:

• Connecting LLMs to your knowledge base (files, docs, spreadsheets, etc.);
• Keeping all the data up-to-date;
• And making sure the answers come from real documents.

RAG integrates external knowledge bases into the text generation process, ensuring the information is precise and up-to-date. It’s like giving the AI a reference book to check facts before answering questions.

Some common RAG use cases are:

• Customer support with company documents;
• Internal documentation search;
• Chatbots to answer customer queries.

Now, how does the retrieval augmented generation (RAG) system work? Let’s see it in the next section.

How Retrieval Augmented Generation (RAG) Works

The technique is pretty straightforward. We can use the same number of steps as there are letters in the acronym “RAG.”

1. External Knowledge Base

The foundation of RAG is a well-curated knowledge base. This involves gathering, organizing, and keeping good quality information. It’s important to keep the knowledge base up-to-date with the newest information.

After collecting the up-to-date information, the files are split into small pieces called chunks. These chunks are then turned into numbers, which we call vectors. An embedding model is used to convert user queries into a numeric format that can be compared against vectors in the knowledge base. These vectors are stored in a special type of database called a vector database. This database can do something called similarity search, which helps find related information quickly.

Vector databases are different from normal databases. They are very good at finding information that is similar to what we’re looking for. This is very important for RAG to work well.

2. Retrieval

When a user asks a question, the system turns user queries into numbers, just like it does with stored information. It then looks for stored content that has a similar structure.

For example, if the user query is about financial quarter results, the system finds parts of documents that talk about financial quarter results. This way, the system can find the most relevant information to answer the question well.

3. Generation

Let’s look at how we ask about opening a support ticket:

• With a normal AI: We just ask, “What is the process to open a support ticket?”
• With RAG: We give the AI more information: “You are a helper for company questions. Use these pieces of information to answer the question. If you don’t know, say so. Use three short sentences. {context_1} {context_2} {context_3} {context_4}”

The question is still: “What is the process for opening a support ticket?” The contexts (context_1, context_2, etc.) are company information that might help answer the question.

The system uses numerical representations to match the context of the query with relevant information. That way, AI can give precise answers using real company information.

Wrapping Up

In this post, we’ve explored how RAG makes generative artificial intelligence smarter by linking it to your own information. This connection tackles major challenges faced by traditional generative models, such as providing outdated or inaccurate responses. With RAG, AI can deliver accurate responses based on your relevant documents.

Additionally, RAG is a game-changer across different fields, like customer service and internal company research, by ensuring AI relies on real facts from your documents instead of making things up. Whether it’s in healthcare, education, or business settings, RAG empowers AI to offer accurate answers.

As AI continues to evolve, RAG will play an even bigger role. It enhances AI’s ability to work with the data you already have, making it more practical and beneficial for everyday tasks.