LLMOps
The operational practices for keeping AI applications running reliably in production — monitoring, evaluation, versioning, cost tracking, and incident response. Similar to DevOps for regular software, but with AI-specific challenges: model behavior can change when models are updated, prompts can drift over time, and outputs are harder to test than deterministic code. LLMOps is what separates teams that ship AI and keep it working from teams that ship it and then scramble when it breaks.
In practice
You ship a Claude-powered feature. Three weeks later, your system prompt is different from what's in production, you don't know which version generated which outputs, and you can't reproduce a bug. LLMOps is the practice that prevents this — version control for prompts, logging for every API call, monitoring for output quality, and a process for safely deploying changes.
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