RAG (Retrieval Augmented Generation): AI and Information Access
In the rapidly evolving field of Artificial Intelligence (AI), a groundbreaking technology is quietly reshaping how we interact with machines and access information. Retrieval Augmented Generation (RAG) represents not just another tech buzzword, but a fundamental shift in AI capabilities that promises to transform various aspects of our digital lives.
Introduced in 2020 by researchers at the University of Washington and Facebook AI Research, RAG addresses critical limitations of traditional Large Language Models (LLMs). While impressive, these models often struggle with up-to-date information and complex queries requiring deep, specialized knowledge.
As we see this revolution, it’s clear that the way we interact with information is changing fundamentally. From transforming customer support to revolutionizing healthcare and education, RAG is not just changing how we interact with AI — it’s changing how AI interacts with our knowledge.
The Mechanics of RAG
RAG operates through two primary phases: retrieval and generation. This dual-phase approach allows for more accurate, context-aware, and up-to-date responses.
