What is LLMOps?
LLMOps (Large Language Model Operations) is the engineering and governance discipline for developing, evaluating, releasing, observing, and improving the complete behavior of applications powered by large language models.
Quick Facts
| Full Name | Large Language Model Operations |
|---|---|
| Specification | Official Specification |
How It Works
Define the Release Unit Before the Pipeline
Create one immutable release ID that resolves to source and build, Prompt, model snapshot, generation parameters, retrieval corpus and index, tool schemas and authorization, output parser, safety policy, evaluator and routing policy. Mutable aliases can select a release but cannot identify it. Assign owners for model risk, application correctness, data governance, security and incident command. The [NIST Generative AI Profile](https://doi.org/10.6028/NIST.AI.600-1) treats risk management as a lifecycle activity, so pre-deployment testing, deployment decisions and post-deployment incidents need connected evidence rather than separate dashboards.
Promote Through an Evidence Ladder
Run deterministic checks first: manifest completeness, hashes, schemas, authorization, tool allowlists, privacy policy and rollback compatibility. Then evaluate representative task, language, tenant and adversarial slices against an approved baseline, with critical failures acting as hard gates instead of disappearing inside an average score. Shadow traffic tests integration without user-visible output; a bounded Canary tests real latency, cost and accepted outcomes. Record the dataset, evaluator, runner, thresholds, approval and release identity for every decision.
Observe Behavior Without Defaulting to Raw Content
Propagate the release ID through request, retrieval, model, tool and policy spans, then measure service health, task outcomes, safety decisions and complete cost by that identity. Prefer event types, hashes, counts, durations and redacted attributes; sample or retain raw Prompt and output only under explicit purpose, access and deletion controls. OpenTelemetry's dedicated [GenAI semantic conventions repository](https://github.com/open-telemetry/semantic-conventions-genai) shows that these attributes continue to evolve, so pin the convention revision and own a translation layer rather than treating field names as a permanent contract.
Rollback the Bundle and Reconcile Side Effects
A model-only rollback can leave an incompatible Prompt, index, parser or tool policy in service. Keep a tested known-good bundle, define traffic drain and state compatibility, and prove that externally visible actions are idempotent, reversible or reconcilable. During an incident, freeze risky automation, preserve release-scoped evidence, restore service and account for messages, payments, tickets or other actions already emitted. Convert the verified failure into a regression case and update the gate, runbook and ownership record.
Key Characteristics
- Treats end-to-end application behavior, not only a prompt or model, as the release unit
- Versions code, prompts, models, retrieval, tools, policies, output contracts, evaluators, and routing
- Separates deterministic contracts, offline evaluation, online experiments, and production monitoring
- Promotes immutable candidates through risk-specific approval, shadow traffic, and guarded canaries
- Connects traces, accepted user outcomes, policy decisions, and cost to an exact release identity
- Pairs full-bundle rollback with side-effect reconciliation and incident-driven evaluation updates
Common Use Cases
- Reproducing which model, prompt, index, tool policy, and parser produced a response
- Blocking a candidate that violates output, authorization, safety, latency, or budget contracts
- Comparing a release with its approved baseline across representative task and risk slices
- Diagnosing production regressions without making unrestricted Prompt logging the default
- Restoring a compatible known-good release and reconciling external actions after an incident
Example
Loading code...Frequently Asked Questions
How is LLMOps different from MLOps?
MLOps operates data, training pipelines, model artifacts, serving, and drift controls. LLMOps inherits those practices and adds prompts, retrieval snapshots, tools, authorization, output contracts, safety policy, evaluators, and routing to the behavior that must be released and operated.
Is LLMOps just prompt management?
No. A Prompt registry manages one asset, while production behavior also depends on code, model revisions, generation parameters, retrieval, tools, authorization, parsers, policies, evaluators, and routing. LLMOps governs the compatible combination.
What should be versioned in LLMOps?
Version every behavior-affecting dependency: source and build, Prompt, model, parameters, corpus and index, tool schemas and policy, output schema and parser, safety policy, routing, evaluation dataset, evaluator, and runner.
What should an LLMOps team monitor?
Monitor service health, release identity, model and retrieval behavior, tool and policy outcomes, accepted user outcomes, and complete cost. Capture raw content only under explicit privacy, access, retention, and sampling controls.
Does LLMOps require a dedicated platform?
No. Teams can begin with reviewed manifests, CI jobs, evaluation reports, dashboards, and runbooks. A platform is useful when it removes a demonstrated coordination or scale bottleneck while preserving portable identities and exportable evidence.