LLMOps

WHAT IT IS LLMOps is the discipline of running production applications on top of large language models, usually ones operated by someone else. It covers provider and model-version management, evaluation harnesses that catch quality regressions, latency and cost per request, guardrails on input and output, fallback behaviour when a provider degrades, and the privacy question of what leaves your boundary with each call. **Not this:** LLMOps is not PromptOps: it owns the whole application around the model — providers, versions, evaluation, cost, guardrails and fallback — of which prompt management is one part. WHY IT EXISTS You do not control the most important dependency in the system. The model can be updated, deprecated, rate-limited or repriced by a third party on their schedule, and the same input can produce different output on two consecutive calls. Traditional deployment safety assumes a deterministic artifact you ship; here the artifact changes without a deploy. LLMOps exists because that inverts the usual assumptions about what a release is and what a regression looks like. WHEN TO USE IT Adopt it the moment an LLM sits in a user-facing path. Even a small feature needs version pinning, a cost ceiling and a defined behaviour for provider failure — those three absences account for most early production incidents in this space. WHO OWNS IT The product engineering team that ships the feature owns its behaviour end to end, including its failure modes; outsourcing the model does not outsource the accountability. Whoever owns the provider relationship owns quota, pricing and deprecation notices. Security and privacy review what is sent to the provider, which is a /privacyops question as much as an engineering one. The product owner owns the quality bar and the decision to disable a feature that falls below it. WHAT GOOD LOOKS LIKE The model and version in use are pinned and recorded, and a provider-side change is detected by your evaluation suite rather than by users. An evaluation set of real cases runs before every release and after provider changes, with a threshold that blocks a regression. Cost and latency are measured per request and per feature, with an alert before the bill becomes the incident. Output passes guardrails appropriate to the risk, and there is a defined, tested behaviour when the provider is slow or down — usually degrading to something honest rather than failing opaquely. What is sent to the provider is documented and defensible. HOW TO IMPLEMENT Pin the model version and record it with every response; without that, no regression is diagnosable. Build a small evaluation set from real cases early — fifty representative examples beat a sophisticated harness that does not exist — and run it in the pipeline. Add per-request cost and latency telemetry next, because this is where surprises are financial. Define and test the fallback path. Add guardrails proportionate to the risk of the output. Treat prompt changes with the versioning discipline in /promptops, and if the application retrieves context, the retrieval side belongs to /ragops. HOW TO ASSESS YOURSELF Ask which model version is in production and how you would know if it changed. Ask what your evaluation suite would catch and what it would miss. Ask what a user sees when the provider times out. Ask what the feature costs per thousand requests. Take the scored self-assessment at /assess, then plan the gaps with /roadmaps.