Use a language model as a bounded component in your application’s decision workflow—not as an unexamined authority. Define the decision it may inform, the evidence and tools it can access, the actions it cannot take, and when a person must review or override its output. Then evaluate and monitor the complete workflow, including the surrounding software and data.
Start by defining the decision and the model’s role
Before choosing a model or writing an integration, describe the decision in plain language. Identify who is affected, what information is relevant, what outcome the application is trying to support, and what happens when the system is wrong or uncertain. The same model output can carry very different risks depending on whether it is a draft for an employee or a trigger for an consequential action.
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- Decision: What specific choice, classification, recommendation, or next step will the model inform?
- Scope: What is the model allowed to consider, and what decisions or actions are outside its authority?
- People and consequences: Who may benefit or be harmed, and what are the likely costs of an incorrect, incomplete, or delayed result?
- Evidence and tools: What user input, records, retrieved information, or application tools can enter the workflow?
- Fallback: What should happen when required information is missing, conflicting, or outside the intended scope?
NIST’s AI Risk Management Framework (AI RMF) calls for documenting an application’s scope in light of system capability and context, and considering expected benefits and costs. The framework is voluntary; NIST’s overview also says AI RMF 1.0 is being revised, so check the current framework and applicable sector or jurisdiction rules for your project. NIST AI Risk Management Framework
Map the whole workflow, not just the model call
A decision-making language model sits inside a larger system. Map the path from input to outcome: data collection, preprocessing, retrieval, model request, output handling, human review, downstream tools, and the final application action. The model may be only one source of risk. A stale record, overly broad tool permission, unclear user interface, or failure to route an uncertain case can also affect the decision.
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For each component, consider whether it affects validity and reliability, safety, security, accountability, transparency, explainability, privacy, or harmful bias in this particular use context. NIST’s AI RMF FAQ describes these as trustworthiness considerations whose relevance depends on the system and context. NIST AI RMF FAQs
Write down the interfaces between components. For example, specify which records may be retrieved, how the application checks that a response relates to the current case, and whether the model can only suggest an action or can invoke a tool. Avoid giving a model access to data or actions that the intended workflow does not require.
Choose an appropriate level of human oversight
Match oversight to the consequences of error and the model’s role. Make the review, escalation, override, and stop conditions explicit before launch. NIST’s AI RMF Core calls for defining, assessing, and documenting human oversight processes. NIST AI RMF Core
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|---|---|---|
| Recommendation only | Produces a suggestion for a person to consider; the application does not act on it automatically. | Who reviews the suggestion, what evidence they see, and how they record acceptance or rejection. |
| Human-approved action | Prepares a proposed action that the application executes only after an authorized person approves it. | Approval authority, information shown before approval, and a way to reject or correct the proposal. |
| Limited automatic action | Can trigger a narrowly defined action under conditions set by the application. | Permitted actions, constraints, escalation triggers, monitoring, and a mechanism to stop or reverse action where feasible. |
This is a design choice, not a universal ranking: what is appropriate depends on the use, affected people, and consequences of error. Document model knowledge limits and acceptable uses in terms that operators can apply. Define what the workflow does with ambiguous or unsupported outputs rather than treating a fluent answer as proof that the answer is correct.
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Evaluate the integrated workflow before release
Test the application as it will be used, not only isolated model responses. A model that performs acceptably on a clean prompt may behave differently when retrieval returns incomplete information, users supply unusual inputs, a tool fails, or the application routes an output incorrectly. NIST calls for measuring performance under conditions similar to deployment and monitoring system behavior after launch. NIST AI RMF Core
Build a representative test set
Use documented cases that reflect the intended users, inputs, context, and edge cases. Include ordinary cases as well as missing or conflicting evidence, out-of-scope requests, ambiguous language, and conditions that should trigger human review or refusal to proceed. Where appropriate, include cases that help reveal uneven outcomes across affected groups.
Define what counts as acceptable
Choose measures tied to the decision and its consequences. Check whether outputs are supported by available evidence, whether required cases are escalated, whether the application takes only permitted actions, and how reviewers handle errors. Set acceptance criteria before testing; do not infer readiness from a few convincing examples.
Exercise dependencies and failure paths
Test the full chain, including data retrieval, third-party software, permissions, user interface, human review, and downstream actions. Verify behavior when a dependency is unavailable or the input does not fit the expected format. Compare candidate models or workflow designs on the same representative cases, with attention to error consequences, oversight needs, privacy and security requirements, traceability, latency, and integration fit.
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NIST describes its work on evaluation probes for agentic AI as an effort to compare model outputs with a human-curated corpus and create structured audit trails linking agent decisions to supporting evidence. It is developing research, not a generally validated or required product. NIST: Building Evaluation Probes into Agentic AI
Make outputs traceable to evidence and action
Keep an audit record appropriate to the application and its privacy and security requirements. A useful record can connect the relevant input and context to the workflow or model version, the output and supporting evidence, any human review or override, and the resulting action. NIST’s evaluation-probe work specifically describes structured audit trails that map agent decisions to supporting evidence. NIST: Building Evaluation Probes into Agentic AI
Traceability helps an organization investigate a disputed or unexpected outcome and understand whether it arose from the model, source data, application logic, or a review step. Decide in advance what information should be retained, who can access it, and how long it should be kept; those choices depend on the application and applicable requirements.
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Monitor, manage changes, and document the lifecycle
Passing pre-release tests is not a permanent guarantee. Monitor relevant workflow behavior after launch, including failures, escalations, overrides, complaints, and changes in data or usage. Set conditions for investigating problems, narrowing the model’s permissions, reverting to a safer workflow, or pausing the feature. Review changes to the model, prompts, retrieval sources, tools, and surrounding application as changes to the system being evaluated.
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NIST organizes its voluntary AI RMF around four functions: Govern, Map, Measure, and Manage. The accompanying Playbook offers suggested actions rather than a rigid checklist, and the framework treats risk management as lifecycle work. NIST AI RMF Playbook The AI RMF 1.0 was released on January 26, 2023; NIST published its Generative AI Profile on July 26, 2024. These publication dates identify guidance versions, not measures of system performance. NIST AI RMF 1.0
In practice, assign owners for the workflow, record its intended use and limits, retain evaluation results and change decisions, and specify who can approve updates or stop the system. The framework is guidance, not a substitute for determining obligations that apply to a particular sector, jurisdiction, or application.
A practical release checklist
- The intended decision, affected users, model scope, and prohibited actions are documented.
- Data sources, third-party components, tools, permissions, and failure paths are mapped.
- Human review and escalation conditions are defined and tested for the relevant stakes.
- The integrated workflow has been tested on documented, deployment-like cases against pre-set acceptance criteria.
- Outputs can be traced to relevant context, evidence, versions, review, and resulting actions at an appropriate level.
- Monitoring, incident response, change approval, and stop or rollback conditions have accountable owners.
NIST’s Generative AI Profile, published July 26, 2024, supplements the AI RMF for generative-AI risks; it can be used alongside the broader lifecycle guidance when the workflow includes a generative model. NIST AI Risk Management Framework
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