What is Cache-Augmented Generation (CAG)?
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Answers ( 2 )
Cache-Augmented Generation (CAG) is a framework designed to optimize large language models (LLMs) for knowledge-intensive tasks. It preloads data and computes key-value (KV) caches, eliminating the need for real-time retrieval. This approach reduces latency and errors compared to traditional Retrieval-Augmented Generation (RAG) methods.
Unlike Retrieval-Augmented Generation (RAG), which relies on real-time retrieval of external documents, CAG preloads data and computes KV caches in advance. This eliminates retrieval latency and document selection errors, making CAG more efficient for tasks where the knowledge base is manageable in size.