What is RAG?

What is RAG?

Before exploring RAG, let’s look at the meaning and origin of the term “RAG” in English. What are the meanings and origins of each word in “RAG”: “Retrieval,” “Augmented,” and “Generation”?

1. Retrieval:
   • Origin: The word “Retrieval” is derived from “retrieve.”
   • Etymology:
     • Derived from “re-“ (again) + “trouver” (French, meaning “to discover” or “to find”). “Trouver” comes from the Latin “tropare,” which means “to find.”
     • “Retrieve” means “to find something that was lost.”
     • In RAG, it is used in the sense of retrieving.
2. Augmented:
   • Origin: The word “Augmented” is derived from “augment.”
   • Etymology:
     • It comes from the Latin “augmentare,” meaning “to increase” or “to enlarge.”
     • Derived from the Latin “augere” (to increase).
     • “Augment” evolved to mean “to increase in size” or “to enhance value.”
     • In RAG, it is used in the sense of augmentation.
3. Generation:
   • Origin: The word “Generation” is derived from “generate.”
   • Etymology:
     • Derived from the Latin “generare,” meaning “to beget” or “to create.”
     • Comes from the Latin “genus” (origin, kind).
     • “Generation” came to mean “creation” or “production,” referring to the process or act of creating something new.
     • In RAG, it is used in the sense of generation.

All these words are derived from Latin and have come together to form the modern concept of “Retrieval-Augmented Generation.”

In artificial intelligence, RAG stands for Retrieval-Augmented Generation. RAG is a technology used in the field of natural language processing (NLP), which combines retrieval-based methods and generation-based methods to create a model that generates more accurate and relevant text.

Key Components of RAG:

1. Retrieval:
   • Role: Retrieves relevant information from a large database based on a given question or text. This stage typically uses parts of retrieved documents or texts.
   • Method: The retrieved documents are selected based on their relevance to the given question. During this process, the similarity between indexed text and the query is calculated to assess relevance.
2. Augmented Generation:
   • Role: Generates text based on the retrieved information. The generated text reflects the content of the retrieved information and may include new information.
   • Method: Typically uses language models like GPT (Generative Pre-trained Transformer) to generate answers to questions. In this process, the model utilizes information from retrieved documents to provide more accurate answers.

Advantages of RAG:

• Efficiency: By leveraging information from retrieved documents, the model does not need to remember all data but can generate text based on a smaller dataset.
• Accuracy: Since the generated text is based on retrieved information, it can provide more accurate and relevant answers.
• Applicability: RAG can be used in various applications where both information retrieval and text generation are needed, such as knowledge-based question-answering systems, conversational AI, and automated report generation.

Applications of RAG:

• Question-Answering Systems: Used to retrieve relevant documents for a user question and generate an answer based on the content of those documents.
• Conversational AI: In conversations on specific topics, it retrieves relevant information (e.g., news, documents) and generates dialogues based on it.
• Text Summarization and Generation: Used to summarize important information from long documents or generate new text on a specific topic.

Three Main Learning Stages of RAG:

• On the right side of the image, the learning stages of the RAG model are explained. The RAG model is divided into the following three stages:

  (1) Pre-training:

  • Description: The stage where the AI model performs pre-training on a large-scale dataset.
  • Role: The initial learning process required for the model to acquire basic language capabilities.

  (2) Fine-tuning:

  • Description: The stage where the model is further specialized for a specific task or dataset.
  • Role: Further training to enable the model to perform better on a specific task.

  (3) Inference:

  • Description: The stage where the trained model is applied to new data to generate predictions and answers.
  • Role: The stage where the model is used in real applications, ultimately producing results.

By combining retrieval and generation, RAG can outperform traditional generative models, especially in applications where the accuracy and relevance of information are crucial.