RAG

The Challenges of Centralized AI

As AI technology is rapidly becoming an essential part of our lives, it is increasingly being centralized in the hands of a few large corporations. The current centralized nature of AI models introduces a "rich gets richer" cycle in which only companies with access to large, labeled datasets can benefit from AI.

What is RAG?

RAG (Retrieval-Augmented Generation) is the process of retrieving relevant contextual information from a data source and passing that information to a large language model alongside the user's prompt. This information is used to improve the model's output by augmenting the model's base knowledge.

When a creator inputs the content they want to customize and initiates a transaction, we handle data chunking and preprocessing based on the category, generate embeddings, and store them in a Vector DB. Then, when a user asks a question, we retrieve the most similar embedding to provide a relevant answer. All the user has to do is input the text, and we take care of setting it up for the best responses.