What is LLMOps and how does it differ from traditional MLOps?

LLMOps extends MLOps by managing prompt engineering, token-based inference, and foundation model lifecycle.

LLMOps introduces new concerns such as prompt versioning, context window management, retrieval augmentation (RAG), hallucination control, and cost per token optimization. Unlike traditional ML models, LLMs are often not trained from scratch but adapted via prompting, fine-tuning, or adapters. Evaluation is probabilistic and requires human-in-the-loop feedback loops and semantic metrics instead of strict accuracy.