Retrieval Augmented Generation in Enterprise Applications

This technical report outlines the limitations of large language models (LLMs) in enterprise applications and presents an approach to enhance retrieval augmented generation (RAG) workflows. It discusses how LLMs often struggle with hallucinations and multi-step reasoning, particularly when dealing with private, on-premises data that differs from their training corpus. The report advocates for a shift from traditional RAG methods, which align more with fast, intuitive thinking (System 1), to a more deliberate and analytical approach (System 2). This involves implementing compound AI systems that assign specialized tasks to different agents, thereby improving retrieval and generation performance. The paper details the need for systematic reasoning and structured workflows in enterprise settings, emphasizing the importance of fact-checking and context alignment to mitigate inaccuracies. It concludes with a vision for future research on enhancing RAG adaptability through compound AI systems, aiming to create more reliable and robust AI solutions for complex enterprise tasks.