What is LLMOps and when do I need it?
LLMOps is the operational practice of developing, evaluating, deploying, and maintaining applications built around large language models. It becomes useful when prompts, model versions, retrieved knowledge, cost, or output quality can change the behavior of a workflow that people depend on.
What does the source say?
Google Cloud distinguishes LLMOps from broader machine-learning operations through the needs of large language models. For a product team, the practical concern is maintaining the whole application rather than merely selecting a model.
How can you apply this?
Choose one production workflow and record its prompt version, model, knowledge sources, evaluation examples, latency, and cost per completed task. Define who reviews regressions and how a previous configuration can be restored. Use a real failure to decide which trace fields are necessary instead of logging everything by default. Record the model and prompt version beside each observed failure so a later configuration change can be investigated rather than guessed.
What should you watch out for?
Request volume and token spend do not tell you whether users completed their task. Pair technical monitoring with a product outcome and protect sensitive trace data.