A Novel LLM Architecture for Intelligent System Configuration

This paper presents a comparative analysis of novel LLM-based architectures designed specifically for system configuration purposes. Generative Artificial Intelligence (Gen AI) has rapidly evolved, offering transformative capabilities in content generation across various domains. Large Language Models (LLMs) stand at the forefront of this evolution, revolutionizing natural language understanding and enabling sophisticated conversational systems. Leveraging the potential of LLMs, our study introduces a novel system architecture centered around an intelligent chatbot tailored to assist learners in complex network configurations. By integrating Generative Pre-trained Transformer-based models with Retrieval Augmented Generation (RAG) and Function Calling features, our architecture aims to provide a co-pilot-like experience, guiding users through understanding requirements and generating configuration scripts. Through a comparative analysis of three LLM architectures, each tailored to handle system network configuration, we evaluate their effectiveness, strengths, and limitations. Our findings offer valuable insights into the potential applications of Generative AI in network operations and highlight avenues for future research and development.