What is Retrieval-Augmented Generation (RAG)

Brief Definition

RAG (short for Retrieval-Augmented Generation) is a technique or software architecture in the field of Artificial Intelligence (AI), designed to optimize the output of a Large Language Model (LLM).

In essence, RAG is a combination of two mechanisms:

• Information Retrieval Mechanism: Searching for data from a highly reliable External Knowledge Base.
• Text Generation Mechanism: Using the LLM’s language understanding and synthesis capabilities to generate natural responses.

The goal of RAG is to provide the LLM with accurate, up-to-date, and specific context, helping the model overcome the limitations of static training data.

Why is RAG needed?

Traditional LLM models often face 3 major problems that RAG can solve:

• Information Updates (Freshness): The LLM does not need Re-training or Fine-tuning yet can still answer the latest information, simply by updating the search database.
• Data Ownership (Proprietary Data): Allows AI to answer questions regarding private enterprise data (internal documents, code base, customer information) that the original model does not know.
• Authenticity (Grounding): Minimizes “Hallucination” (AI fabricating information) by forcing the AI to cite or rely on actual text passages found.

Operational Architecture

The process of handling a question in RAG proceeds as follows: