What is Retrieval-Augmented Generation (RAG)?

Welcome to a new era of AI. We’ve all seen how large language models (LLMs) can generate creative and human-like text, but they often struggle with accuracy and can’t access up-to-the-minute information. This is where Retrieval-Augmented Generation, or RAG, comes in.

RAG is an AI framework that combines the power of a language model with an external knowledge base. Instead of relying solely on its pre-trained data, the model first “retrieves” relevant documents or information from a separate, trusted source (like a database or the internet) and then uses that information to “augment” its answer.

This approach offers several key benefits:

• Improved Accuracy: It significantly reduces the chance of “hallucinations” (when an AI fabricates information).
• Access to Current Data: It can be connected to real-time data sources, providing up-to-date responses.
• Source Citation: The model can cite its sources, allowing users to verify the information.

RAG is a foundational concept in the development of more reliable and trustworthy AI systems, and it is quickly becoming a standard for building powerful AI applications.