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Move an AI feature to production only when its purpose and risks are defined, it meets use-case-specific release criteria, its deployment can be traced and controlled, and an accountable team is ready to monitor and respond. Safety is not a one-time approval: it depends on how the feature is used and must be reassessed as the system or context changes.
Start by defining what the feature is for
A prototype that produces plausible output is not yet a production-ready product. Before evaluating it, write down the boundaries within which it is supposed to work. NIST’s Generative AI Profile recommends analyzing intended purpose in context, including the users, expected impacts, lifecycle assumptions, limitations, and evaluation measures.
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- Purpose: What task does the feature support, and what should it not be used to do?
- Users and setting: Who will use it, whether the use is internal or external, and where it will appear in the workflow.
- Consequences: What benefit is expected, who could be harmed by an incorrect or inappropriate result, and how serious that harm could be.
- System boundaries: Which models, prompts, data sources, retrieval systems, tools, vendors, and application components affect the result?
- Assumptions and limits: What conditions must hold for the feature to work, and what known situations should lead to a refusal, escalation, or fallback?
- Measures: How will the team judge task performance, reliability, safety, and other outcomes that matter for this use?
These decisions define the scope of the evaluation and the launch. If the feature moves to a different user group or workflow, its original assessment may no longer fit.
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Turn the important risks into release criteria
Use a risk framework to organize questions, not to claim that a feature is certified safe. NIST describes the AI Risk Management Framework (AI RMF) as voluntary and use-case agnostic; the framework is being revised. Its trustworthiness considerations span validity and reliability, safety, security and resilience, accountability and transparency, explainability, privacy, and fairness, including the management of harmful bias. The NIST Generative AI Profile, published July 26, 2024, adds generative-AI-specific considerations such as privacy, human-AI configuration, information security, component integration, and harmful bias.
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For each material risk, decide what evidence is needed before launch and who will judge it. Convert those decisions into criteria that can be checked, such as a defined evaluation set, a review of failure cases, an access-control test, or an approved fallback for uncertain results. Numerical thresholds, mandatory human review, and rollback limits cannot be set universally: they should reflect the feature’s domain, possible impact, and the organization’s risk tolerance and obligations.
A useful release record ties each criterion to its evidence and decision owner. If a risk has no practical mitigation or the team cannot tell whether a criterion has been met, treat that as an unresolved launch decision rather than assuming the prototype’s performance is sufficient.
Evaluate the feature before and after launch
Test the system against the work it will actually do, not just a few impressive demonstrations. Evaluation should happen before deployment and continue in production. The right mix of automated tests and human assessment depends on the intended use and the consequences of failure.
Cover task performance and failure modes
For generative AI, test the output risks relevant to the application, including unsafe, biased, off-topic, malicious, or factually inaccurate responses. Also measure task performance and reliability under expected conditions. Include difficult and unusual cases that could plausibly occur in use, rather than treating a strong average score as proof that important failures are absent.
Check grounded answers against their sources
If the feature uses supplied documents or retrieved sources, assess whether responses are supported by that material. A grounding check can compare claims in an answer with the text made available to the system. This is different from checking whether an answer sounds convincing: unsupported statements may still be fluent.
Use evaluation results as a release decision
Keep the tested cases, results, known gaps, and decision record with the version being considered for deployment. A failure should lead to a change in the system, a narrower intended use, an explicit mitigation, or a decision not to release; it should not disappear into an average score. Establish how new production evidence will update those evaluations.
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Promote changes through controlled environments
Separate development, non-production, and production so experiments and candidate changes do not reach users by accident. Google Cloud’s enterprise AI/ML blueprint describes this kind of environment separation alongside an MLOps workflow for testing and deploying models. Its blueprint is an implementation example, not a requirement to use Google Cloud or a particular architecture.
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- Develop: Make and test changes in a development environment, with access limited to the people and services that need it.
- Validate outside production: Promote a candidate to a non-production environment and run the relevant application, security, and evaluation checks using an appropriate test setup.
- Review and approve: Record what changed, what evidence was produced, and who approved promotion. Include changes to prompts, data, retrieval, models, and connected components, not only application code.
- Deploy to production: Use a repeatable, auditable deployment path. CI/CD can make deployments more consistent and reduce manual errors, but the pipeline itself still needs appropriate controls and review.
- Confirm operation: Check that the deployed version is the approved one and that its monitoring, access boundaries, and response ownership are in place.
A controlled path makes it easier to investigate what reached users and to distinguish an application change from a model, prompt, or data change.
Instrument the complete application
Monitoring a model in isolation can miss the source of a bad result. Log and monitor the application end to end, including inputs, outputs, and the components that contributed to each response. Google Cloud’s deploy-and-operate guidance emphasizes application-level monitoring, component lineage, and alerts for drift, skew, or performance decay.
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Maintain enough lineage to connect a result to the relevant model, prompt or configuration, retrieved material, tools, and other artifacts or parameters used to produce it. Logging inputs and outputs can help explain failures, but the data may contain sensitive information; define what is retained, who can access it, and how it is protected in line with privacy, security, and organizational requirements.
Begin investigation at the application level: identify the affected user journey and response, then trace the components involved. This helps teams avoid attributing a failure to the model when the cause may be retrieval, a source document, integration logic, or another part of the system.
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Production ownership requires watching both what the feature does and whether the service is operating as intended. NIST’s Generative AI Profile and Google Cloud’s AI/ML security guidance support continuous monitoring, though the exact measures depend on the feature.
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| Monitor | Examples to track | Why it matters |
|---|---|---|
| Output behavior | Task quality, safety issues, factual errors, and relevant user or reviewer feedback | Shows whether the feature continues to meet its intended purpose without unacceptable output failures. |
| Service health | Latency, errors, traffic, and infrastructure health | Reveals outages or degradation that can make a feature unusable or affect the user experience. |
| Security posture | Access to models, datasets, and pipeline components; unexpected permission changes; suspicious request patterns | Can expose unauthorized access or changes that undermine the system’s protections. |
| Model and application behavior over time | Drift, skew, or performance decay in the application and its components | Indicates that real-world inputs or results may be departing from the conditions under which the feature was evaluated. |
Every alert that could require action needs an owner and a response path. Define who investigates, how the team assesses impact, and what actions are available, such as limiting a feature, routing work to another process, or reverting a change. NIST recommends incident-response planning for third-party generative-AI technologies and policies for continuous monitoring of third-party systems. Align those plans with applicable legal and organizational requirements, and rehearse them so responsibilities are clear during an incident.
Reassess when the system or its context changes
A previously approved feature can acquire a different risk profile after a change. Re-evaluate when the model, prompt, data, retrieval setup, vendor, or application changes materially, and when the purpose, user group, or deployment context shifts. A change that seems small in code may alter what users see or what information the system relies on.
Set a review cadence that reflects the feature’s impact and rate of change. NIST’s lifecycle guidance supports ongoing risk management, but the sources do not establish one universal reapproval interval. Keep the chosen cadence, triggers for an out-of-cycle review, and accountable owner explicit.
Make the launch decision auditable
Before release, the cross-functional team should be able to show how the intended use, evaluation evidence, deployment controls, and operational response fit together. A practical decision record can capture:
- the approved purpose, users, deployment context, assumptions, and limitations;
- the material risks and the release criteria chosen to address them;
- evaluation results, known failure modes, and unresolved limitations;
- the version and components approved for production, with the promotion record;
- the logs, lineage, alerts, and response owners needed to investigate and act;
- the events or changes that require reassessment.
If any of these are missing, the team has a gap to resolve or explicitly accept before launch—not a reason to treat a successful prototype as production-ready.
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