How Cache Augmented Generation Transforms LLMs
AI Summary
The video discusses Cache Augmented Generation (CAG), which involves preloading a language model with an entire knowledge base into its context window. This method allows the model to utilize proprietary information or new data that emerged after its original training. The key difference between CAG and simply manually loading documents into prompts lies in the concept of caching. In CAG, the model encodes documents during a single forward pass into a Key Value Cache (KVC), allowing subsequent prompts to leverage this cached knowledge without needing to reprocess it. This approach is particularly effective when dealing with a stable set of knowledge that fits within the model’s context window and will be reused across multiple prompts.