Understanding and Building Operational Excellence for Large Language Models

Abstract

Effective life cycle management of large language models (LLMs) ensures their reliability and adaptability in production environments. The study's relevance is due to the rapid growth in the use of large language models in various industries, accompanied by challenges such as high computational requirements, risks of generating false results (“hallucinations”), and algorithmic bias. It is established that traditional MLOps methods do not provide adequate quality control and scalability, which requires the development of specialized operational approaches for LLM. The study aims to form an operational framework for LLMOps that covers all stages of the life cycle of large language models, from data processing to real-time monitoring and support. The research methods are based on an interdisciplinary analysis of current practices of implementing large-scale language models, identifying problematic aspects of their use, and developing practical recommendations for effective system management. The main stages of the operational framework are identified, including data preparation, model development and deployment, and control of results at the monitoring and support stages. The most critical aspects are integrating multi-level monitoring, compliance with ethical standards, and introducing automated algorithms to reduce the frequency of false positives. The study's results confirm that the proposed operational framework increases the reliability of language models in high-load environments and ensures their adaptation to dynamic changes in the query structure. The use of distributed computing methods, resource optimization, and post-processing verification helps minimize the risks associated with the performance and accuracy of model responses. Simulation stress tests are recommended to check the system's stability during peak load periods. Conclusions. The paper emphasizes the importance of continuously auditing models' operations to ensure their transparency and compliance with regulatory requirements. Prospects for further research include optimizing retraining processes and implementing energy-efficient computing resource management methods.