Systems Engineering of Large Language Models for Enterprise Applications

This report provides a comprehensive systems engineering analysis of Large Language Model (LLM) adoption in enterprise environments, emphasizing the need for a structured implementation approach to ensure success. It examines key aspects of LLM deployment, including customization techniques such as Parameter-Efficient Fine-Tuning (PEFT), Low-Rank Adaptation (LoRA), and Retrieval-Augmented Generation (RAG), while addressing challenges related to computational resource management, model safety, and ethical considerations. The study outlines a framework covering the entire LLM lifecycle, from initial needs assessment to deployment and maintenance, with a focus on data management strategies, infrastructure requirements, and integration methods. Additionally, it offers a comparative analysis of fine-tuning existing models versus developing in-house solutions, providing practical guidance for overcoming implementation challenges. The findings underscore the importance of balancing technical capabilities with operational constraints and ensuring robust security, compliance, and ethical standards.