Tag: RAG

The article discusses how costs associated with Retrieval-Augmented Generation (RAG) systems can spiral due to fragmented control and local decision-making in AI workflows. It emphasizes the need for a centralized control layer to manage resource allocation, caching, retrieval depth, and model calls, ultimately aiming to reduce unpredictability in AI spending.