RAG (Retrieval-Augmented Generation)
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.

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