Technical Paradigm Comparison - DevOps focuses on software-centric development with a straightforward feedback loop to test code functionality [1] - MLOps focuses on model-centric operations addressing data drift, model decay, and continuous retraining due to environmental changes [1] - LLMOps focuses on foundation-model-centric systems utilizing pre-trained foundation models rather than training from scratch [2] Operational Monitoring And Evaluation - MLOps tracks data drift, model decay, and accuracy [2] - LLMOps monitors system safety, grounding, and cost-effectiveness rather than just binary correctness [2] - LLMOps evaluation loops operate non-linearly, where failed evaluations simultaneously inform prompt improvements, context enhancement, or fine-tuning [3] Core Engineering Pillars - LLMOps infrastructure establishes prompt versioning and RAG (Retrieval-Augmented Generation) pipelines as first-class citizens [3] - LLM engineering roadmaps encompass eight key pillars spanning from prompt engineering to observability and safety [4][5] - LLM system components specifically include hallucination detection, bias and toxicity monitoring, token usage and cost tracking, and human feedback loops [5]