RAG: The Future of Generative AI and How It Works.

Imagine being able to have an AI assistant that not only understands your requests but also has instant access to real-time, relevant information to answer them perfectly. That’s the promise of Retrieval-Augmented Generation (RAG), a cutting-edge approach that’s revolutionizing the way artificial intelligence interacts with the world.

So, what exactly is RAG? Unlike traditional large language models (LLMs) that rely solely on their internal knowledge base, RAG takes things a step further. It combines the power of LLMs with an external knowledge source, allowing them to access and leverage additional information on the fly. This means your AI assistant can tap into news articles, research papers, or even your company’s internal documents to provide more accurate, up-to-date, and contextually relevant responses.

Here’s how it works:

RAG consists of two main components: a Retriever and a Generator.

The retriever is responsible for finding and ranking the most relevant documents or passages that answer the user question, based on a similarity measure. The generator is responsible for producing a natural language response, based on the user question and the retrieved documents or passages.