Don’t do RAG: CAG is all you need for knowledge tasks

Cache-Augmented Generation (CAG) is emerging as a faster alternative to Retrieval-Augmented Generation (RAG). While RAG dynamically searches databases when queries arrive, CAG preloads relevant knowledge into the model's context and reuses cached computations, dramatically reducing response times. RAG excels when data changes frequently and massive datasets are involved, while CAG works best for stable information and repetitive queries where speed is critical. Most production systems now use hybrid approaches, combining CAG's speed for common queries with RAG's freshness for dynamic information needs.