This repository implements CAG, a retrieval-free alternative to RAG that leverages extended LLM context windows and cached runtime parameters. Instead of querying a vector database at inference time, all relevant knowledge is preloaded into context, enabling direct generation without retrieval latency. The approach was published at ACM Web Conference 2025 as a short paper investigating the relationship between model performance and context length.