LLMOps

LLMOps is the discipline of operating large-language-model (LLM) applications in production with the same rigor that MLOps brings to traditional machine learning. It spans the lifecycle—model selection, prompt and retrieval versioning, automated evaluation, continuous deployment, cost tracking, and observability. Pipelines integrate vector databases for semantic search, feature stores for prompt variables, and secret managers for API keys, all wrapped in CI/CD that runs unit tests on prompts and RAG accuracy before merging to main. LLMOps platforms such as LangSmith, PromptLayer, and Weights & Biases trace every prompt, token, and latency metric, while guardrails filter PII and toxicity. Canary releases and automatic rollback handle model drift when providers update GPT-4 or Gemini weights. By uniting DevOps, MLOps, and prompt engineering, LLMOps turns hacky prototypes into scalable, compliant, and cost-efficient generative-AI services.