RAG (RETRIEVAL-AUGMENTED GENERATION) AS A NEW PARADIGM FOR ENTERPRISE AUTOMATION

Abstract

This article examines Retrieval-Augmented Generation (RAG) as an emerging paradigm for enterprise-grade corporate automation and knowledge-centric information systems. While the rapid adoption of large language models (LLM) has significantly expanded the capabilities of natural language interfaces in business environments, LLM-centric automation remains constrained by fundamental limitations, including hallucinations, static knowledge embedded in model parameters, and insufficient domain specificity for regulated and high-risk enterprise scenarios. These limitations restrict the reliable use of standalone LLM in knowledge-intensive business processes where accuracy, traceability, and compliance are critical.

The paper analyzes the evolution of corporate automation from rule-based systems and classical machine learning approaches to LLM-oriented solutions and argues that RAG represents a qualitative architectural shift rather than an incremental improvement. By decoupling knowledge storage from the generative model and integrating external retrieval mechanisms, RAG enables controlled access to up-to-date corporate knowledge bases, policy documents, and operational data without requiring model retraining. In this architecture, LLM primarily function as reasoning engines, while authoritative knowledge remains externally managed, auditable, and continuously updated.

Special attention is given to modern RAG extensions, including Ontology-Grounded RAG (OG-RAG), Retrieval-to-Augmented Generation (R2AG), and holistic knowledge retrieval approaches. These methods improve semantic alignment between retrieval and generation, reduce factual inconsistencies, and enhance robustness in complex enterprise environments. The article also considers the integration of RAG with multi-agent orchestration layers, highlighting their role in supporting scalable, modular, and business-oriented AI systems.

From an enterprise perspective, RAG is positioned as an operational foundation for knowledge-centric automation across document analysis, compliance support, decision-making, and customer service. At the same time, key challenges are identified, such as increased infrastructure complexity, latency overhead, and the lack of standardized business-oriented evaluation metrics. Overall, the study positions Retrieval-Augmented Generation as a core enabling technology for the next generation of corporate automation systems that balance adaptability with controlled enterprise knowledge management.