Operationalizing Generative AI: A Comprehensive Framework for LLMOps Excellence

Abstract

The rapid emergence of large language models (LLMs) has ushered in a new era of generative artificial intelligence (AI), promising transformative applications across customer service, content generation, research automation, and beyond. With increased adoption, organizations face significant challenges in establishing operational capabilities to deploy, maintain, and scale LLM-driven services effectively. This paper synthesizes the extant literature on LLM Operations (LLMOps), integrating recent frameworks, maturity models, practices, and empirical analyses to propose a cohesive, theoretically grounded, and practically actionable operational framework. In particular, we examine definitions and boundaries of LLMOps vis-à-vis MLOps and FMOps, analyze maturity models for generative AI operations, discuss deployment strategies in cloud-based and distributed environments, and explore the organizational and economic implications of large-scale LLM adoption. Employing a rigorous literature review and conceptual synthesis methodology, we identify recurring themes, operational challenges, best practices, and gaps. Our findings reveal that successful LLMOps requires multi-dimensional readiness — encompassing infrastructure, continuous integration/ continuous deployment (CI/CD) pipelines, governance, cost optimization, prompt engineering, and human–AI collaboration. We articulate a multi-layered maturity framework — from ad hoc experimentation to enterprise-grade LLM ecosystem — and offer strategic recommendations for enterprises transitioning from proof-of-concept to production-grade LLM services. Limitations include scarcity of robust empirical performance data, evolving industry practices, and reliance on grey literature. We conclude by outlining future research directions to validate the framework with empirical studies, evaluate total cost of ownership in long-term deployments, and investigate ethical, governance, and human–centered aspects of LLMOps.