Retrieval-Augmented Generation (RAG)

Introduction

Retrieval-Augmented Generation (RAG) is a technique that makes Large Language Models (LLMs) smarter and more reliable by giving them access to external information before they answer questions. Instead of relying only on its training data, a RAG-enabled LLM can look up relevant, up-to-date information from a trusted knowledge base to provide a more accurate and contextually aware response.

Pros and Cons of RAG

RAG offers significant advantages by reducing hallucinations and allowing for data freshness, but it also introduces complexity in the retrieval and indexing process.