RAG can make AI models riskier and less reliable, new research shows

Retrieval-Augmented Generation (RAG) is rapidly emerging as a robust framework for organizations seeking to harness the full power of generative AI with their business data. As enterprises seek to move beyond generic AI responses and leverage their unique knowledge bases, RAG bridges general AI capabilities and domain-specific expertise. Hundreds, perhaps thousands, of companies are already using RAG AI services, with adoption accelerating as the technology matures. The bad news: According to Bloomberg Research, RAG can also vastly increase the chances of getting dangerous answers. Before diving into the dangers, let's review what RAG is and its benefits.