RAG: The Overhyped GenAI Pattern?
Retrieval Augmented Generation (RAG) has become a popular approach in generative AI. However, this post argues that RAG suffers from critical flaws in high-stakes, regulated industries. The core issue is that RAG exposes users directly to LLM hallucinations by presenting the LLM's output without sufficient validation. The author suggests RAG is better suited for low-stakes applications like vacation policy lookups, while semantic parsing offers a safer alternative for high-stakes scenarios. RAG's popularity stems from ease of development, significant funding, industry influence, and improvements over existing search technologies. The author stresses that in high-stakes scenarios, direct reliance on LLM output must be avoided to ensure data reliability and safety.