How RAG Architecture is Transforming AI
The rise of contextual data for LLMs
Since Large Language Models (LLMs) were unleashed on the world in the form of AI chatbots, their use in our everyday lives has grown. They have spawned a whole new part of the economy, driven by brand-new AI businesses and the integration of AI into existing technologies.
The biggest challenge for most businesses looking to integrate AI into their technology is making it relevant for their specific audience.
Without millions of dollars at your disposal or a team of highly talented AI engineers, training your own LLM from scratch is out of the equation for most.
So how do we embed AI into existing technologies using existing LLMs? The answer is to utilize Retrieval Augmentation Generation — or RAG for short.
Let’s dive into how RAG is transforming the use of AI.
What is Retrieval Augmentation Generation (RAG)?
Most LLMs are trained on huge amounts of data to answer questions on almost any topic. However, all of the model’s knowledge is then built into the model.
There are two problems with this approach.
- The model’s knowledge becomes out of date very quickly.
