LLMOps vs MLOps

MLOps covers the lifecycle of machine-learning systems: data, training, evaluation, deployment, monitoring and governance. LLMOps extends those practices to applications built around large language models. A team may not train foundation-model weights, but it still manages prompts, model versions, context assembly, retrieval indexes, fine-tuning data, safety rules and application code. These assets can change system behavior as much as a conventional model update. Prompt templates need versioning and evaluation. A small wording change can alter responses, tool use or refusal behavior. Retrieval-augmented generation adds document ingestion, chunking, embedding models, indexes and retrieval ranking; each affects what evidence reaches the generator. Track corpus and index versions, access rules and freshness. If documents contain sensitive or untrusted content, retrieval must preserve permissions and defend against prompt injection. LLM evaluation often combines automated metrics, task-specific test sets, model-based judging and human review. Each method has limitations: reference answers may not cover acceptable variations, and an evaluator model can share biases or miss factual errors. Build cases around key capabilities and known failures, then compare candidate prompts and models under consistent conditions. Safety, privacy and tool-use checks matter alongside fluency. Production monitoring includes latency, availability, input/output token counts, cost, refusal patterns and user outcomes where measurable. Provider behavior may change, and a model identifier may not be fully reproducible if the service updates behind an alias. Log enough metadata for review while protecting personal data and secrets. MLOps concepts such as staged deployment, observability, incident response and governance still apply. LLMOps is not a replacement discipline with one standard toolchain; it adapts established operational controls to prompt-driven, retrieval-heavy and provider-dependent applications.