Layman’s AI

Explaining RAG in Layman’s Terms

Simplest Explanation!

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Have you ever wondered how AI applications, including large language models (LLMs), can provide up-to-date information or even recall knowledge that wasn’t part of their original training? This is where Retrieval-Augmented Generation (RAG) comes in.

Imagine you’re talking to an AI that can reference current events, recent research, or even your personal data (securely) to provide tailored responses. RAG makes this possible by retrieving relevant information from external sources and generating responses based on it. In this article, we’ll break down RAG in simple terms, explain how it works, and see why it’s such a game-changer.

What is Retrieval-Augmented Generation (RAG)?

At its core, RAG is a hybrid approach that enhances AI by allowing it to access external knowledge bases to generate more accurate and relevant responses.

Most LLMs, like ChatGPT or Google Gemini, are trained on vast datasets but only up to a certain point in time. That means they lack access to real-time updates or specialized knowledge that wasn’t part of their training.

For example, a model trained before 2023 wouldn’t know about the latest developments in AI or a scientific…