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
LLMOps extends DevOps and MLOps to dependencies that make generative application behavior change: application code, prompts, model and provider revisions, generation parameters, retrieval corpora and indexes, tool schemas and authorization policy, output contracts, safety policy, evaluators, and routing. A mature process binds those dependencies to an immutable release identity, runs deterministic contracts before probabilistic evaluation, promotes approved candidates through shadow or canary delivery, attributes production traces and cost to the exact release, and restores a compatible known-good bundle during incidents. LLMOps is an operating model rather than a requirement to buy one platform. See the <a href="https://qubittool.com/blog/enterprise-llmops-architecture-guide">enterprise LLMOps architecture guide</a> for the complete lifecycle.
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.