What is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation (RAG) is an advanced AI framework that helps Large Language Models (LLMs) provide more accurate, relevant, and up-to-date answers by incorporating data from external sources into the generation process.

While LLMs like GPT have transformed natural language processing by automating tasks like customer support and content generation, they face challenges:

• Stale Knowledge: LLMs rely on pre-trained datasets, limiting their ability to handle real-time queries or respond with domain-specific knowledge.
• Hallucinations: LLMs sometimes generate plausible-sounding but incorrect or fabricated information.
• Lack of Context: They struggle to integrate live, proprietary, or domain-specific data into their outputs.

These issues are especially critical in industries that demand precision and up-to-date knowledge, such as healthcare, finance, legal services, and customer support. This is where RAG bridges the gap; it helps LLMs access dynamic information from external sources.