  The rapid integration of Large Language Models (LLMs) into various industries
presents both revolutionary opportunities and unique challenges. This research
aims to establish a scalable and efficient framework for LLM customization,
exploring how DevOps practices should be adapted to meet the specific demands
of LLM customization. By integrating ontologies, knowledge maps, and prompt
engineering into the DevOps pipeline, we propose a robust framework that
enhances continuous learning, seamless deployment, and rigorous version control
of LLMs. This methodology is demonstrated through the development of a
domain-specific chatbot for the agricultural sector, utilizing heterogeneous
data to deliver actionable insights. The proposed methodology, so called
DOLLmC, not only addresses the immediate challenges of LLM customization but
also promotes scalability and operational efficiency. However, the
methodology's primary limitation lies in the need for extensive testing,
validation, and broader adoption across different domains.
