CAG vs RAG: Which One is Right for You?

This podcast episode explains Cache-Augmented Generation (CAG), a technique used to improve the speed and accuracy of Large Language Models (LLMs). The speaker contrasts CAG with Retrieval-Augmented Generation (RAG), highlighting CAG's speed advantage due to pre-loading knowledge into the model's memory, but also noting its limitations regarding context window size and potential cost. The episode details how CAG works, using key-value pairs, and discusses when it's most beneficial (e.g., frequently asked questions, specific reports), comparing it to having memorized parts of a textbook versus having access to the entire book. The speaker also mentions the possibility of combining CAG and RAG for a hybrid approach and notes that Google's Gemini API uses a similar technique.