Bridging Human and AI Decision-Making with LLMs: The RAGADA Approach

Abstract

The Retrieval Augmented Generation Algorithmic Decision Alignment (RAGADA) architecture is an advancement in AI-augmented decision-making for corporate environments. This paper discusses RAGADA’s innovative architecture that merges RAG and Multi-Agent System (MAS) with sophisticated business algorithms and dynamic interfaces, enhancing natural language interaction between AI systems and users. This fusion extends AI’s reach, facilitating adaptable decision-making tools for leaders, in line with evolving business strategies and ethical standards. Experimental validation of RAGADA within the banking sector, involving diverse stakeholder groups ranging from customers to business and ethical managers, confirms its effectiveness. The system adeptly translates natural language inquiries into actionable insights, thereby improving the user experience and decision-making transparency. This validation underscores RAGADA’s potential to transform stakeholder engagement and demonstrates a leap in utilizing AI for strategic and ethical business management.