Llmops
LLMOps is the set of practices, tooling and team habits for building, deploying, monitoring and improving applications built on large language models, extending MLOps to a world where the main artifact is no longer a model trained in-house but a prompt, a retrieval pipeline, a tool set and a hosted or self-served foundation model wired together. Its subject is the whole system around the model: what text goes in, what comes out, how good it is, how safe it is and what each call costs.
LLMOps inherits MLOps goals but changes where the work sits. Classic MLOps revolves around training pipelines, feature stores, model registries and drift monitoring for models a team trains itself. In an LLM application the weights are usually someone else's, changed on the provider's schedule, and the behaviour is steered mostly by natural-language instructions. Iteration therefore moves from retraining to editing prompts, chunking documents, choosing retrieval settings and swapping models. Outputs are free text with no single correct label, so quality cannot be reduced to one accuracy number, and each request is billed by tokens in and out rather than by fixed compute. The result is a pipeline of change management, evaluation, observability and governance suited to non-deterministic text.
Prompt versioning treats instructions as code that ships. A prompt template, its few-shot examples, the model name, the temperature and the tool definitions together determine behaviour, so changing any one is a release. Teams keep them in version control or a prompt registry, tag every production call with the version that produced it, and keep the ability to roll back. Without this, a regression is untraceable, because nobody can say which of several overlapping edits changed the output. Hosted providers also retire or update model snapshots, so pinning an explicit model version, where offered, is part of the same discipline.
Evals and guardrails are the quality and safety layers. Evaluation means a curated set of real or realistic cases with checks that range from exact-match and schema validation to rubric scoring by another model, run before every change and sampled in production. Guardrails are runtime controls placed around the model: input filters for prompt injection and personal data, output validators that enforce a JSON schema or block disallowed content, limits on which tools can be called and with what arguments, and fallbacks when a check fails. They reduce risk but are not a complete defence; classifiers have false positives and misses, and determined adversaries probe them, so they sit alongside least-privilege tool access, not in place of it.
Cost tracking and observability make the economics and the behaviour visible. Providers price per token, usually at different rates for input and output, so an unnoticed change such as a longer system prompt, an extra retrieved chunk or an agent loop that runs twice as many steps can multiply the bill. LLMOps records tokens, latency, model, prompt version, retrieved context and tool calls for each trace, attributes spend to features or customers, and applies caching, shorter contexts, smaller models for easy requests and rate limits. Tracing standards such as OpenTelemetry and purpose-built platforms, among them LangSmith, Langfuse and MLflow's LLM features, collect these records.
A common mistake is to treat LLMOps as a tool purchase, or to assume that a good demo means a production-ready system. Impressive examples hide a long tail of rare failures that only a maintained eval set and production sampling can reveal, and model updates can alter behaviour overnight with no change in the application code. The discipline is mostly unglamorous: version everything, measure before and after, and keep humans in the loop for review.
Read LLMOps as MLOps rebuilt around text: the model is a rented, shifting component, so the durable assets are the versioned prompts, the eval sets, the guardrails and the cost and quality telemetry that keep the application honest.