CAG (Cache-Augmented Generation) is an innovative enhancement technique for language models aimed at addressing issues such as retrieval delays, errors, and complexity inherent in traditional RAG (Retrieval-Augmented Generation) methods. By preloading all relevant resources and caching their runtime parameters within the model context, CAG can generate responses directly during inference without requiring real-time retrieval. This approach significantly reduces latency, increases reliability, and simplifies system design, making it a practical and scalable alternative. As the context window of large language models (LLMs) continues to expand, CAG is expected to be applicable in more complex scenarios.