Changing an AI-assisted writing workflow can alter wording, citations, review responsibilities, data exposure, or the ability to reproduce a result. A useful change record should let another person answer three questions later: what changed, why was it accepted, and how can it be reversed?
Identify the change before making it
Give the change a unique ID and record the requester, owner, date, reason, affected content types, and intended outcome. Capture the prior tool version, model or service name, reusable instruction set, source-selection policy, export format, connected repositories, review checklist, and publication gate. Do not copy confidential source material into the change log; use approved references or redacted samples.
This mirrors established configuration-control practice. NIST SP 800-53 control CM-3 calls for documenting proposed changes, justification, implementation, testing, review, disposition, and retained records (NIST SP 800-53 Revision 5). The writing workflow may not be a regulated information system, but the structure is still useful: it prevents a vague note such as “updated automation” from becoming the only history.
Preserve a reproducible test packet
Use a small, rights-cleared test set that represents routine work and known edge cases. Preserve the input brief, permitted source URLs, expected factual points, style requirements, prohibited claims, and acceptance rubric. Record the tool settings and workflow steps needed to rerun it. Then save both the prior and proposed outputs with timestamps.
Provenance belongs in that packet. NIST's Generative AI Profile describes provenance as tracking the origin and history of data and synthetic content and notes that metadata can include creators, dates, modifications, and sources (NIST AI 600-1). For each sample, record who reviewed it, which claims were checked, which sources supported them, and which material edits were made after generation.
Test quality and accessibility
Compare factual accuracy, citation coverage, unsupported-claim count, reading level, required disclosures, formatting, and reviewer time. If the workflow includes an authoring interface, include keyboard access, accessible editing views, error correction, and support for accessible output. Those are among the concerns summarized by the W3C Authoring Tool Accessibility Guidelines overview (W3C ATAG overview). Record failures as well as passes; a clean summary without the failed cases cannot explain the decision.
Keep operational evidence
Save the approval or rejection, approver, effective date, deployment identifier, monitoring window, owner of follow-up, and rollback procedure. NIST's log-management guidance describes logs as records of events and emphasizes planning for their management (NIST SP 800-92 Revision 1 initial public draft). Apply that idea proportionately: preserve run IDs, status, validation results, and exceptions, but avoid logging sensitive inputs or credentials.
After the change, sample real work during the monitoring window. Compare the observed outcome with the baseline and record any correction. A good evidence folder contains a one-page decision record, redacted test packet, results table, approval, deployment reference, monitoring notes, and tested rollback instructions. It should be understandable without access to the original operator's memory or personal account.
Sources
- Security and Privacy Controls for Information Systems and Organizations — National Institute of Standards and Technology, Sept. 23, 2020
- Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile — National Institute of Standards and Technology, July 26, 2024
- Authoring Tool Accessibility Guidelines — World Wide Web Consortium
- Cybersecurity Log Management Planning Guide — National Institute of Standards and Technology, Oct. 11, 2023