AI writing workflow handoff notes should include the permitted use cases, prohibited data, authoritative sources, tool and model identifiers, input template, output format, human review duties, factual verification method, copyright and attribution rules, approval owner, saved evidence, cost boundary, and exception path. The notes must distinguish an AI draft from reviewed and publishable content.
Define scope, roles, and boundaries
State which writing tasks the workflow supports and which decisions remain human-owned. Name the requester, operator, subject reviewer, editor, approver, and incident contact. List prohibited inputs such as secrets, customer data, private records, or material without authorized use. Record the approved accounts and workspace; never embed credentials in the handoff.
NIST's AI Risk Management Framework says policies should differentiate roles and responsibilities for human-AI configurations, document knowledge limits and human oversight, and define the specific tasks the AI system supports (NIST AI RMF Core). Reflect those controls in plain language for the actual workflow.
Record sources and transformation steps
Describe where source material comes from, how its authority and date are checked, and which claims require more than one source. Save source URLs and brief extracted facts separately from the draft. Explain the input template, required tone, structure, audience, and the changes a reviewer must make before approval.
Record the provider, product, model or configuration label visible to the user, date, prompt version, and any tools or retrieval sources used. Do not claim a fixed model behavior from an old run; note that outputs can vary and must be reviewed each time.
Specify review and release gates
Create checks for factual support, missing context, unsafe instructions, private information, misleading certainty, copied phrasing, links, dates, arithmetic, and compliance with house style. The NIST Generative AI Profile provides suggested actions for governing, mapping, measuring, and managing generative-AI risks across the lifecycle (NIST Generative AI Profile). Use applicable controls without presenting the framework as a guarantee of correctness.
Define state labels such as draft, fact-checking, editorial review, approved, scheduled, and published. Only the named approver may move work through the final release gate. Preserve the reviewed version and claim evidence, not just the raw output.
Document failures and recovery
List common failure modes: unavailable sources, unsupported claims, prompt contamination, private-data exposure, missing disclosure, broken formatting, provider outage, or cost threshold reached. State when to retry, when to switch to manual work, and when to stop and escalate.
The handoff is complete when a new operator can create a draft from approved sources, verify its claims, identify risky inputs, apply the required disclosure, and stop before publication without authorization. If the operator cannot reproduce the evidence trail, label the workflow incomplete rather than ready.
Sources
- AI RMF Core — National Institute of Standards and Technology
- Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile — National Institute of Standards and Technology, July 26, 2024