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This article presents the concept of LLMOps—applying large language models to AIOps—by analyzing prompt challenges, introducing the LogPrompt engine for log analysis, describing a prompt‑learning data flywheel with CoachLM optimization, reporting experimental results, and outlining future multi‑modal directions.
The article presents an AI‑driven microservice governance platform that employs a multi‑agent architecture—Director, Coder, Ops, and Diagnosis agents guided by SOP‑encoded prompts—to enable natural‑language, full‑process interactions, LLM‑based root‑cause analysis, and a continuous data‑flywheel that evolves models through large‑scale dialogue evaluation.
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The proposed unified platform centralizes prompt creation, evaluation, and iteration for large‑model applications, offering one‑stop hosting, metric‑driven testing, seamless resource integration, model switching, fine‑grained traffic control, and an automated data‑flywheel with QEP scoring, cutting optimization cycles from weeks to days while paving the way for advanced fine‑tuning techniques.