What is Retrieval-Augmented Generation (RAG)?

Integrate the advanced capabilities of generative large language models (LLMs) with an innovative AI framework.

Introduction

Retrieval-Augmented Generation (RAG) is an innovative AI framework that integrates the strengths of traditional information retrieval systems with the advanced capabilities of generative large language models (LLMs). This combination allows RAG to generate text that is not only highly accurate but also up-to-date and contextually relevant to specific queries.

How does Retrieval-Augmented Generation work?

RAG operates through a series of steps designed to enhance the outputs of generative AI:

1. Retrieval and Pre-processing: RAG uses advanced search algorithms to query external data sources such as web pages, databases, and knowledge bases. The retrieved information is then pre-processed, which includes tasks like tokenization, stemming, and removal of stop words.
2. Generation: The pre-processed information is integrated into the pre-trained LLM. This integration provides the LLM with a richer context, enabling it to produce more accurate, informative, and engaging responses.