A Study on the Architecture and Operation of Intelligent Knowledge Platforms Using LLMs and AIOps

Abstract: Traditional knowledge platforms often rely on keyword-based search and manual system management, resulting in poor contextual understanding and operational inefficiencies. This paper presents a study on the architecture and operation of intelligent knowledge platforms integrating Large Language Models (LLMs) and AIOps technologies. LLMs enable semantic understanding, contextual reasoning, and intelligent response generation, while AIOps enhances operational reliability through automated monitoring, anomaly detection, predictive analysis, and self-healing mechanisms. The proposed layered architecture combines semantic intelligence with operational intelligence to improve scalability, user experience, and system resilience. The study also discusses key challenges, applications, and future research directions for AI-driven knowledge platforms.

Keywords: Large Language Models (LLMs), AIOps, Intelligent Knowledge Platforms, Semantic Intelligence, Automated IT Operations, Artificial Intelligence, Context-Aware Systems, Intelligent Automation