Effective Methods for Adapting LLM to the Agricultural Business Domain

Abstract

The article discusses the current challenges of applying large language models (LLMs) in the agricultural business sector and proposes modern approaches to address these issues. Despite the high effectiveness of LLMs in natural language processing, their adaptation to the tasks of the agricultural industry involves a number of difficulties. Key problems include the formation of specialized training corpora, balancing the quality of responses with computational costs, objective evaluation of model quality, and their integration into existing agricultural information systems. Practical approaches to solving these problems are discussed, including fine-tuning models on specialized data, computational optimization methods, and the use of hybrid architectures (in particular, RAG). The main areas of LLM application are also analyzed: text generation, search engine improvement, analysis of user reviews, and customer support automation. The research aims to improve the accuracy, relevance, and personalization of model responses in tasks related to forecasting, analysis, and automation of processes in agriculture. The proposed solutions contribute to the effective integration of LLMs into the infrastructure of the agricultural sector, enhancing decision-making quality, forecasting, and business process automation.