HomeBlogWhat is RAG Retrieval Augmented Generation in AI and How It Works

What is RAG (Retrieval Augmented Generation) in AI and How It Works?

In the context of generative AI what does RAG stand for?

Ever wondered how AI could come up with exactly correct and relevant answers in no time? What is RAG in AI, and how does it differ from the traditional model of Generative AI? More importantly, how can it advance your AI solutions to improve efficiency and customer satisfaction?

In this article, we’ll plunge into these questions:

  • The RAG AI definition
  • How retrieval augmented generation works
  • The architecture of RAG
  • Its key benefits and challenges
  • RAG practical use cases

You will clearly understand how RAG can be effectively implemented in your company to transform the use of Generative AI, driving business scaling, growth, and increased sales.

TL;DR: RAG GenAI enhances AI with real-time information

  • Retrieval-Augmented Generation (RAG) improves AI responses by retrieving relevant, up-to-date information from external sources.
  • Benefits of RAG GenAI include accurate responses, reduced hallucinations, domain-specific outputs, and cost-effective deployment.
  • Challenges and limitations involve integration complexity, data quality, and synchronization delays.
  • Use cases range from customer support, virtual assistants, and personalized recommendations to medical diagnosis, making RAG a versatile tool in various industries.

What does RAG stand for in AI?

Retrieval-Augmented Generation (RAG) is a method in Generative AI that enhances content generation by retrieving and integrating the most relevant, up-to-date information from external databases or knowledge sources. Unlike traditional models that rely solely on pre-trained knowledge, RAG dynamically pulls in external data to produce more accurate and contextually relevant outputs.

How does retrieval augmented generation work?

After understanding the RAG meaning in generative AI, the next logical question is, "How does RAG work?" Retrieval-Augmented Generation (RAG) systems operate by linking user prompts with relevant external data, pinpointing the piece of information that is most semantically aligned with the query. This relevant data serves as context for the prompt, which is then fed into a language model (LLM) to generate a precise, contextual response.

This retrieval augmented generation diagram shows how the system combines external information with user prompts to provide appropriate outputs:

In essence, retrieval augmented generation works by first gathering and processing relevant data from external sources, breaking it down into manageable chunks, embedding it into vector representations, processing the user query to identify pertinent information, and finally generating a detailed and accurate response using a language model. What sets RAG apart from traditional LLMs is its ability to utilize both the user’s query and the up-to-date data simultaneously, rather than relying solely on the user input and the pre-existing training data.

Step 1: Data gathering

The first step in implementing RAG GenAI is gathering all the necessary data for your application. For instance, if you’re developing a virtual health assistant for a healthcare provider, you would collect patient care guidelines, medical databases, and a compilation of common patient inquiries.

Step 2: Data breakdown

Next comes data chunking, where you divide your information into smaller, manageable segments. If you have a comprehensive 200-page medical guideline, you might break it down into sections that address different health conditions or treatment protocols. This method ensures that each data chunk is focused on a specific topic, making it more likely that the retrieved information will directly answer the user’s question. It also boosts efficiency, allowing the system to quickly access the most relevant pieces without wading through entire documents.

Step 3: Document embeddings

Once your data is chunked, it’s time to convert it into vector representations, known as document embeddings. This involves transforming the text into numerical forms that capture its semantic meaning. The document embeddings help the system comprehend user queries and match them with relevant information based on meaning rather than a simple word-for-word comparison. This approach ensures that the responses provided are pertinent and aligned with what the user is asking.

Step 4: Query processing

When a user submits a query, it also needs to be converted into an embedding. It’s crucial to apply the same model for both document and query embeddings to maintain consistency. Once the user query is embedded, the system compares it with the document embeddings to identify which chunks are most similar to the query embedding. Techniques like cosine similarity and Euclidean distance are used to find these matches, ensuring that the retrieved chunks are the most relevant to the user’s inquiry.

Step 5: Output formulation with an LLM

Finally, the selected text chunks, along with the original user query, are processed by a language model. The model combines this information to generate a coherent response tailored to the user’s needs.

To streamline this entire process of generating responses with the large language models, you can utilize a data framework like LlamaIndex. This solution helps you efficiently manage the flow of information from external data sources to language models like GPT-3, making it easier to develop your own LLM applications.

Retrieval augmented generation architecture

The RAG architecture consists of two key components: a retriever and a generator. The two-part approach boosts text generation by integrating retrieval capabilities right into the model. This method allows for responses that are both well-informed and contextually relevant, thanks to verified data.

The retriever and generator components collaborate seamlessly to ensure high-quality and accurate replies. This makes RAG architecture large language model a valuable tool for applications like chatbots and question-answering systems, which require advanced language understanding and effective communication.

Let’s dive deeper into how each component plays a crucial role in the RAG system’s functionality and explore the RAG model architecture diagram, showing how the retriever gathers data for the generator to produce accurate responses.

RAG model architecture diagram

The retriever component

Function: The retriever is responsible for locating relevant documents or pieces of information to help address a query. It takes the input query and searches a specified database to find information that can assist in forming a response.

Types of retrievers:

  • Dense retrievers: These types utilize neural network techniques to generate dense vector embeddings of the text. They are particularly effective when the focus is on understanding the underlying meaning of the text rather than matching the exact words, as these embeddings capture semantic similarities.
  • Sparse retrievers: These ones rely on term-matching strategies, such as TF-IDF or BM25. They are particularly adept at locating documents that contain exact keyword matches, making them valuable for queries with unique or rare terms.

The generator component

Function: The generator is a language model that creates the final text output. It incorporates both the input query and the context retrieved by the retriever to produce a coherent and relevant response.

Interaction with the retriever: The generator collaborates with the retriever; it depends on the context provided by the retriever to shape its responses. This connection ensures that the output, besides being plausible, is also very detailed and accurate, adding quality to the response.

The retrieval augmented generation (RAG) architecture integrates the generation and retrieval together seamlessly, r