LLMOps
LLMOps is the discipline of running large language models in production: managing prompts, context, costs, latency and outputs with the same rigour MLOps applies to predictive models, adapted to models that answer in words and can say almost anything.
No jargon, no math
In plain words, what is it?
Classic MLOps assumes a model with one number to watch: accuracy. LLMOps runs models that answer in words, so the pipeline around them changes. What goes in: prompts and context, versioned like code. Each candidate is tested against a golden set: real questions with ideal answers, scored by a rubric or by a judge model. Only a version that beats the one in production ships, wrapped in guardrails: refusals, filters, allowed tools. Once live, every conversation is logged: input, retrieved documents, final answer, latency, cost. Dashboards watch quality scores, spend per conversation and error rates, and fresh questions keep flowing into the golden set. Where it breaks: a silent prompt edit, a stale knowledge base or a cheaper model swap can all erode answer quality with no error message. Evaluation has to run continuously, not once at launch.
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Version. Prompts, context templates and model settings live in version control.
- 02
Evaluate. Each candidate answers a golden set of real questions and is scored against ideal answers.
- 03
Guard. The winning setup ships with guardrails: allowed tools, refusals, output filters.
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Log. Every conversation stores input, retrieved context, answer, latency and cost.
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Watch. Dashboards track quality scores, spend and errors; fresh tickets feed the golden set.
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Improve. When scores slip, the loop restarts with a new prompt or model.
Terms that live nearby.
MLOps
MLOps is the set of practices, tools and pipelines that take a machine learning model from a data scientist's notebook into production and keep it running reliably: versioning data and code, training, evaluating, deploying and monitoring the model like any other business-critical software.
ReadPrompt Versioning
Prompt versioning is treating prompts as code: every change is numbered, stored in a repository, tested against a fixed set of examples, and rolled back when a new version regresses.
ReadGuardrails
Guardrails are the checks and limits wrapped around an AI model so its behaviour stays inside your rules: they screen what goes in, constrain what the model can do, and verify what comes out before it reaches a person or a system.
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