Large Language Models (LLMs) like ChatGPT have revolutionized our interactions with machines. They serve as companions, answer simple questions about life, and assist in writing documents like this one. However, LLMs are expensive to train and maintain, limiting their availability to a few capable companies. Beyond the cost, they have several notable drawbacks:
Retrieval Augmented Generation (RAG) addresses the drawbacks of LLMs by retrieving relevant documents when a user queries information that the LLM cannot answer. This approach combines document retrieval with the generation capabilities of LLMs or other generative models. The potential benefits for businesses are significant, as this method can unlock numerous opportunities and use cases.
I will also explore broader questions related to RAG from scientific literature and solutions developed by other companies. I am excited to explore this avenue.