Quick answer
An AI feature is ready for a controlled launch only when you have evidence that it performs its defined task, handles unacceptable failures appropriately, and can be stopped when conditions change. Test the complete workflow—not just the model—and establish who reviews outputs, when the system must abstain, and what triggers rollback.
Do not use a single accuracy score as launch approval. Record error severity, performance on difficult cases, unresolved limitations, and whether reviewers can actually catch consequential mistakes.
The checklist below is a practical way to turn evaluation evidence into a release decision. Its suggested controls are not universal thresholds or certification requirements. NIST’s AI Risk Management Framework is voluntary, and it treats risk management as a lifecycle activity rather than a one-time launch exercise. (nvlpubs.nist.gov)
1. Define the task and the decisions it must not make
Start with a one-page description of the feature’s permitted use. NIST’s framework calls for understanding the deployment context and documenting human oversight. (nvlpubs.nist.gov)
For your launch brief, specify:
- User: Who will use the output, and what relevant expertise do they have?
- Input: Which document types, languages and formats are supported?
- Output: What exactly should the feature produce?
- Action: What may happen because someone relies on that output?
- Boundary: Which requests must be blocked or referred elsewhere?
- Owner: Who approves release and accepts the remaining limitations?
For a contract-summary feature, a useful boundary might be:
“Prepare a source-linked draft of specified contract terms for an internal reviewer. Do not recommend signing, determine enforceability, or send the summary to a counterparty without approval.”
That is narrower and more testable than “understand contracts.”
Practical check: Write down a tempting but unsupported use. If the interface makes that use easy—for example, presenting a draft as a definitive legal assessment—change the interface or restrict access before launch.
2. Build a task-specific test set with trustworthy references
OpenAI’s official evaluation guidance recommends task-specific tests that reflect production use, include difficult cases, and combine metrics with human judgment. This is vendor guidance, not an independent assurance standard. (developers.openai.com)
For the contract example, build two clearly labeled collections:
- Representative cases: The kinds of documents the feature is intended to handle.
- Challenge cases: Deliberately difficult inputs that probe known failure modes.
Suggested challenge cases include missing attachments, conflicting amendments, scanned tables, repeated clause headings, ambiguous wording, and important terms located near the end of a document. Include unsupported languages or formats to test rejection, not just successful processing.
For each case, have an appropriate domain reviewer prepare a reference record containing:
- Required facts and their source locations.
- Qualifications or exceptions that must be preserved.
- Information that is genuinely absent.
- Ambiguities that should remain unresolved.
- The expected escalation or abstention behavior.
Keep development examples separate from a held-out evaluation set. OpenAI’s guidance also recommends held-out testing and expanding evaluations as new failure cases appear. (developers.openai.com)
Practical check: Resolve disagreements in the reference answers before using them to grade the system. Do not treat an ambiguous contract as if it has one obvious answer merely to simplify scoring.
3. Classify errors by consequence, not just frequency
NIST’s framework considers risk in relation to both the likelihood of an event and the magnitude of its consequences. (nvlpubs.nist.gov)
Apply that principle by agreeing on an error rubric before examining release results. A suggested rubric for the contract feature is:
| Error class | Illustrative failure | Proposed response |
|---|---|---|
| Presentation | Awkward wording that preserves meaning | Correct through ordinary review |
| Material factual error | Wrong party, amount, deadline or obligation | Block approval until corrected |
| Material omission | A relevant exception or amendment is missing | Return for source verification |
| Boundary failure | The feature recommends signing or claims legal validity | Investigate the failed restriction |
| Access or confidentiality failure | Content appears to an unauthorized user | Stop the affected workflow and investigate |
These are proposed product controls, not legal classifications.
Report material errors separately from presentation errors. Also distinguish a mistake in the generated draft from a mistake that survives the entire review process.
A weighted score may help compare versions, but make certain failures independent release blockers. For example, your team might decide that any known unresolved authorization failure prevents launch regardless of the summary-quality score.
Practical check: Attach a concrete failed example to every important metric. The release approver should be able to see what “material omission” means in practice.
4. Make abstention and escalation observable behaviors
Abstention means withholding an answer when the workflow cannot support it. For this feature, define abstention explicitly rather than relying on a general instruction to “be careful.”
Suggested triggers include:
- Required pages or attachments are unavailable.
- Text extraction leaves a relevant clause unreadable.
- A statement cannot be connected to supporting source text.
- Amendments appear to conflict.
- The request falls outside the supported task.
The response should explain the blocker and the next step: “The referenced schedule was not supplied. Upload it or ask the reviewer to inspect the complete document.”
Do not equate confident wording with verified correctness. A 2024 Nature study investigated semantic entropy—uncertainty across the meanings of multiple generated answers—to detect a particular class of hallucinations. The authors explicitly state that their approach does not guarantee factuality or detect systematically wrong outputs. (sebastianfarquhar.com)
For your evaluation, track both:
- Coverage: How often the workflow produces a draft rather than withholding it.
- Material-error rate among drafts: How often the drafts it does produce contain consequential mistakes.
Also inspect unnecessary abstentions and errors that were not escalated. Otherwise, a feature could appear reliable simply because it refuses most useful work.
Practical check: Test the destination of every escalation. Confirm that the reviewer receives the source, the uncertainty and the reason for referral.
5. Design human review as a complete workflow
NIST’s framework calls for defined, assessed and documented oversight processes. A reviewer’s name on a release checklist is not enough to demonstrate that process. (nvlpubs.nist.gov)
For the contract-summary feature, make review a required product step:
- Show the draft beside the complete source document.
- Connect each material statement to the relevant passage.
- Highlight missing information and unresolved ambiguities.
- Require verification of obligations, dates, amounts and exceptions.
- Allow rejection or correction without pressure to approve.
- Prevent unapproved drafts from triggering downstream actions.
Test reviewers using deliberately flawed drafts. Check whether they identify material mistakes, not merely whether they complete the approval form.
Record where review fails. Perhaps citations are difficult to navigate, an amendment is hidden, or the reviewer lacks the necessary expertise. Treat those as workflow problems to address before expanding access.
For contract interpretation or signing decisions, this checklist does not establish legal suitability. Seek appropriately qualified advice for the actual documents, jurisdiction and intended use.
Practical check: Decide what happens when the review queue has no available qualified reviewer. For this proposed workflow, the default should be to hold the output—not silently bypass approval.
6. Work through a hypothetical contract-summary failure
This example is fictional and illustrates a test design, not observed performance or legal advice.
Assume an operations team wants summaries of renewal and notice terms. Every draft requires source verification before business use.
The fictional test document contains:
“Either party may prevent automatic renewal by giving written notice at least thirty days before the renewal date.”
Elsewhere, an amendment replaces “thirty days” with “sixty days.” A referenced schedule containing the renewal date is missing.
A flawed draft says:
“Cancel at least thirty days before renewal.”
Evaluate it against the reference record:
| Check | Finding |
|---|---|
| Amendment handling | The draft missed the replacement notice period |
| Terminology | “Cancel” is not faithful to the narrower supplied wording |
| Completeness | The renewal date cannot be established from the provided files |
| Source support | A citation to the original clause would not support ignoring the amendment |
| Escalation | The missing schedule should be flagged |
The expected draft should identify the amended notice period, preserve the distinction between preventing renewal and other forms of termination, and state that the renewal date is unavailable. It should remain pending human review.
This implements NIST’s recommendation to verify sources and citations in generative-AI outputs. A citation must support the statement; its presence alone is not the check. (nvlpubs.nist.gov)
Then repeat the case with different layouts and amendment placement. The objective is to test the defined behavior, not to memorize one document.
7. Monitor the deployed version and rehearse rollback
NIST’s generative-AI profile includes post-deployment monitoring, incident response, recovery, change management and decommissioning. (nvlpubs.nist.gov)
For this feature, maintain a release record covering the model identifier where available, prompt, extraction process, retrieval configuration, review interface and relevant dependencies.
Suggested monitoring fields include:
- Material errors found in reviewed drafts.
- Errors discovered after approval.
- Failed or unsupported source references.
- Abstentions and their causes.
- Rejected drafts and recurring reviewer corrections.
- Review backlog and unavailable-reviewer incidents.
- Newly encountered input types.
Collect only the information needed for those checks. Avoid retaining complete sensitive contracts merely because full-content logging is convenient.
Write rollback triggers before launch. Suggested triggers include an authorization failure, approval bypass, a recurring material-error pattern, or an untested dependency change.
Rehearse disabling generation, holding queued drafts and returning to a manual process. Specify who can activate each control and what happens to outputs already distributed.
Practical check: Confirm that rollback changes the user-facing workflow—not just a dashboard setting.
8. Make the release decision from an evidence packet
OpenAI recommends continuous evaluation as an application changes. NIST warns against extrapolating capabilities from narrow or anecdotal assessments. Together, these support treating release approval as conditional evidence, not permanent proof of reliability. (developers.openai.com)
Use this final sign-off checklist:
- Supported users, inputs, outputs and prohibited uses are documented.
- Representative and challenge cases have reviewed reference answers.
- Results identify material failures, not just an aggregate score.
- Abstention and escalation have been tested end to end.
- Reviewers have demonstrated source-checking and rejection.
- Unresolved limitations are visible to release approvers.
- Monitoring and incident responsibilities have named owners.
- Rollback and manual fallback have been rehearsed.
- Relevant model, prompt, data and interface changes require reevaluation.
Choose among hold, limited pilot, or controlled release. A limited pilot should have explicit users, restricted inputs, required review and stop conditions.
The decisive question is not “Does the AI usually sound right?” It is: “Do we have sufficient evidence and workable controls for this particular use—and a reliable way to stop when those controls fail?”