Why Retrieval Augmented Generation (RAG)?

Advanced generative AI models have limitations when it comes to producing accurate responses. They can only generate answers based on their training data and often produce incorrect or irrelevant information when faced with unknown topics. This lack of awareness is referred to as “hallucinations, bias, or nonsense”.

A new approach called Retrieval Augmented Generation (RAG) aims to address this limitation by combining retrieval-based methods with generative models. RAG retrieves relevant data from external sources in real-time and uses it to generate more accurate and contextually relevant responses.

This integration enables generative AI models to produce better results, making them a powerful tool for various applications. One of the key strengths of RAG is its adaptability, allowing it to be applied to different types of data, including text, images, and audio.