Human-in-the-Loop LLMOps: Balancing automation and control

This paper explores the essential role of Human-in-the-Loop (HITL) strategies in Large Language Model operations (LLMOps), offering a comprehensive framework for balancing automation with human judgment in enterprise AI deployments. As LLMs become integral to business workflows, organizations face growing risks related to bias, factuality, ethics, and compliance. This article examines HITL practices across prompt engineering, review systems, feedback loops, governance structures, and tools, identifying successful implementation patterns and performance metrics. It concludes with forward-looking guidance on emerging standards, scalability, and responsible oversight. The framework empowers enterprises to deploy AI systems that are both powerful and accountable, augmenting automation with control to ensure alignment with human values and organizational goals.