MLOps vs LLMOps: What Changes When the Model Is an LLM
MLOps operates machine-learning systems; LLMOps extends those practices to prompts, context, retrieval, providers, tools, evaluations and runtime behavior around large language models.
LLMOps Playbook
Rollback StrategyMonitoring (Quality, Drift)Versioning (Prompts, Models)Canary & A/B ReleasesEvaluation Harness
LLMOps Playbook
Keep LLM behavior stable across changes.
- Versioning for prompts and models
- Evaluation harness + quality gates
- Canary/A-B releases + monitoring
- Fast rollback procedures
Core idea
Treat prompts and model routing like production code: versioned, tested, monitored, and reversible.
Related
LLM Capability Reference Model AI Rollback RunbookRelated Concepts (LLMOps)
Prompt Regression Test Sets for LLMs Evaluation Rubrics Fallback Strategy Cost Spike ControlArticles
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