RAG vs. CAG: Augmenting AI Model Knowledge

The YouTube video from IBM Technology explains two primary methods for augmenting the knowledge of large language models: Retrieval Augmented Generation (RAG) and Cache Augmented Generation (CAG). RAG involves retrieving relevant information from an external knowledge base to supplement the model's training data for a specific query. CAG, conversely, preloads the entire knowledge base into the model's context window. The video details the workings, capabilities, and trade-offs of each approach, including accuracy, latency, scalability, and data freshness. Finally, it presents hypothetical scenarios to illustrate when each method, or a hybrid approach, might be most suitable.