Intelligent automation of economic processes based on retrieval-augmented generation and large language models

Abstract

The article is devoted to the theoretical and methodological substantiation of the concept of intelligent automation of economic processes based on the integration of Retrieval-Augmented Generation (RAG), Large Language Models (LLM), Prompt Engineering, and Automatic Engineering technologies. The modern digital economy is moving from technical automation to cognitive automation, in which self-learning systems are emerging that can adapt to environmental changes, analyze the results of their own activities, and generate new economic solutions. RAG acts as a cognitive intermediary between generation and data retrieval, ensuring the factual reliability of analytical results. LLMs provide semantic interpretation of information and create conditions for natural-language scenario modeling. Prompt Engineering determines the quality of interaction between humans and the system by transforming users’ analytical intentions into a formalized query structure. Particular attention is paid to Automatic Engineering as a meta-level of cognitive management that ensures automated prompt improvement, reconfiguration of generation parameters, development of decision metamodels, and formation of digital twins of management processes. A multilevel cognitive-engineering model of economic management is proposed, which includes strategic, cognitive-technical, self-learning, and reflexive levels. This architecture forms a closed cognitive cycle of “generation – evaluation – optimization – updating,” which ensures the system’s capacity for reflexive self-learning and evolutionary development. The practical significance of the research lies in creating a methodological basis for implementing agentic AI solutions in strategic planning, risk forecasting, and enhancing the intellectual resilience of economic systems in the digital economy. Keywords: intelligent automation, Retrieval-Augmented Generation (RAG), Large Language Models (LLM), Prompt Engineering, Automatic Engineering, cognitive architecture, digital economy, strategic management, self-learning economy, artificial intelligence.