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glossary

LLMOps

The operational discipline for building and running applications powered by large language models: prompt management, evaluation, guardrails, cost and latency control, and monitoring of non-deterministic outputs.

In depth

LLMOps adapts MLOps for applications built on large language models, where teams usually consume foundation models through APIs or fine-tune them rather than training from scratch. The operational challenges are distinctive: outputs are non-deterministic text, so quality is judged with evaluation suites that combine golden datasets, programmatic checks, human review, and LLM-as-judge scoring. Prompts become production artifacts that must be versioned, tested, and rolled out like code, since a one-line wording change can alter behavior across the application. Production concerns include guardrails against prompt injection and unsafe outputs, per-request token cost tracking, latency management with streaming and caching, and fallback strategies when a provider degrades. Observability platforms capture full request traces, prompt, retrieved context, model parameters, response, and user feedback, so regressions can be diagnosed. LLMOps also covers managing retrieval pipelines for RAG systems and continuously testing new model versions, because providers update models and yesterday's prompts may behave differently tomorrow.

Why it matters

LLM applications fail in novel ways: hallucinations, prompt injection, runaway token bills, and silent regressions when a provider updates a model. Teams without LLMOps discipline discover these failures from angry users. As generative AI moves into production everywhere, these skills are in explosive demand.

Real-world example

example.txt

A customer-support copilot team versions prompts in Git and runs every change against a 500-example evaluation set scoring accuracy and tone with an LLM judge. When the model provider releases a new version, the eval suite catches a regression in refund-policy answers before rollout, and the team adjusts the system prompt and pins the new version only after scores recover.

Tools related to LLMOps

LangSmithLangfuseOpenAI EvalsWeights & BiasesGuardrails AIHelicone

Interview questions

  1. How does LLMOps differ from traditional MLOps?
  2. How do you evaluate the quality of LLM outputs at scale?
  3. What is prompt injection and how do you defend against it?
  4. How would you control cost and latency in a high-traffic LLM application?
  5. How do you safely upgrade to a new model version in production?
  6. What should an observability trace for an LLM request contain?