The direct answer is to rerun a fixed, representative test set through the updated workflow and compare the results with a recorded baseline before allowing normal publishing. Verify more than writing quality: test source handling, factual claims, privacy boundaries, permissions, human approval, exports, and rollback.
Define exactly what changed
Record the date, provider or model version, prompt or policy revision, connector change, document template, user roles, publishing integration, and settings before testing. Keep the previous approved configuration or export where the product permits it. If several components changed, test them separately when possible so a failure has a traceable cause.
NIST's AI Risk Management Framework applies trustworthiness considerations across design, development, use, and evaluation. The NIST AI Resource Center specifically supports testing, evaluation, verification, and validation. That means an update should trigger both content checks and system checks.
Build a representative test pack
Use at least five safe cases: a simple factual article, a source with conflicting dates, a document containing irrelevant instructions, a request with prohibited sensitive data, and an opinion prompt that must be labeled and still include supported facts. Add one long document and one malformed input if the workflow accepts uploads.
For every case, define expected behavior before running it. List required sources, claims that must be verified, content that must be rejected or redacted, the correct reviewer, and whether publishing should remain blocked. Do not grade the output only by style; fluent text can still fail the factual or privacy requirement.
Compare quality and evidence
Open every citation and confirm it supports the nearby claim. Check names, numbers, dates, quotations, and current product behavior. Measure missing citations, unsupported claims, broken links, duplicated sections, and formatting errors. Compare the updated result with the same case from the prior approved version rather than relying on memory.
NIST's Generative AI Profile recommends robust, iterative, documented testing, evaluation, validation, and verification. Save test identifiers, outputs, reviewer decisions, and defects without retaining unnecessary sensitive prompts.
Verify controls around the writing
Confirm that writers cannot publish without approval, reviewers can see sources and changes, and publishing credentials remain isolated. Test that a connected document cannot override the workflow's instructions, request secrets, or redirect publication. Verify logs record who changed a draft without exposing full confidential inputs.
Test export and rollback. Export a draft with sources, restore or reopen it, and check that headings, links, metadata, and approval state remain intact. Revert to the previous configuration in a non-production space and confirm the documented rollback steps still work.
Release in stages
Start with a small set of low-risk articles and require heightened review. Monitor rejection reasons, factual corrections, source failures, and publishing errors. Stop the rollout if the new workflow weakens a required gate, loses source evidence, exposes restricted input, or publishes without the named approver.
The update passes only when the fixed test set meets the same or stricter acceptance criteria, failures are understood, rollback is proven, and a responsible person signs off. “The output looks better” is useful feedback, but it is not a verification record.
Sources
- AI Risk Management Framework — National Institute of Standards and Technology
- NIST AI Resource Center — National Institute of Standards and Technology
- Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile — National Institute of Standards and Technology, July 26, 2024