Empowering AI Models: Fine-Tuning RAG Data and Human Feedback

An organization that continually increases the volume of its RAG data will reach the threshold of non-parametric data (not pretrained on an LLM). At that point, the mass of RAG data accumulated might become extremely challenging to manage, posing issues related to storage costs, retrieval resources, and the capacity of the generative AI models themselves. Moreover, a pretrained generative AI model is trained up to a cutoff date. The model ignores new knowledge starting the very next day. This means that it will be impossible for a user to interact with a chat model on the content of a newspaper edition published after the cutoff date. That is when retrieval has a key role to play in providing RAG-driven content.

Companies like Google, Microsoft, Amazon, and other web giants may require exponential data and resources. Certain domains, such as the legal rulings in the United States, may indeed require vast amounts of...