RAG vs. CAG: Solving Knowledge Gaps in AI Models

RAG and CAG are two approaches for enhancing AI models with external knowledge. RAG retrieves relevant documents from a database for context, while CAG preloads all knowledge into the model's context. RAG excels in scalability and accuracy with dynamic data, while CAG offers lower latency and is suitable for fixed knowledge bases.