Definition
LLMOps is the set of practices used to evaluate, deploy, observe, govern, and maintain applications built around large language models. It covers model and provider selection, prompts, context assembly, retrieval, tool schemas, safety controls, evaluations, cost and latency, tracing, version drift, feedback, and incident response.
Distinguish it from nearby terms
LLMOps overlaps MLOps but often manages externally hosted models that the application team did not train. That shifts operational attention from training pipelines toward runtime context, provider behavior, nondeterministic outputs, tool use, and end-to-end evaluation. Agent operations extends the scope again to stateful action, permissions, and long-running execution.
Check your understanding
Version the whole behavior-producing configuration, not only the model name. Prompts, tools, retrieval data, sampling settings, provider snapshots, and harness code can all change outcomes.