The short answer
Teams get a lightweight prompt-card format with purpose, inputs, prohibited data, expected output, test cases, owner, and last-review date.
The decision standard is simple: preserve the source, state the limits, and make the next human check obvious. A useful article should reduce uncertainty without pretending that every unknown has been resolved.
What to examine
Saving clever prompts is easy. Maintaining reliable workflow instructions requires versioning, examples, approved data boundaries, model compatibility, and a way to retire stale entries.
Start with scope. Identify the product, account, audience, jurisdiction, data, and decision involved. Then separate what was directly observed from what a vendor, researcher, regulator, or commentator says. Record dates because AI products, access rules, and prices change quickly.
A prompt that worked once is not a controlled process. Model updates, tool permissions, and upstream data can change outcomes.
A practical way to do it
- Define the job and the decision the output supports.
- Store passing and failing examples with the prompt version.
- Assign an owner and review after model or policy changes.
Keep the worksheet or test record with the draft. If another editor cannot reproduce the check from the saved evidence, the article is not ready.
Editorial guardrail
Do not fill a missing fact with a plausible sentence. Mark it as unknown, find a stronger source, narrow the claim, or remove it. Commentary belongs in a clearly labeled paragraph after the reported facts, not inside them.
Primary-source reading list
These are starting points, not automatic support for every sentence. The publishing editor must open each cited page and confirm the claim it supports on the day of review.
Bottom line
Teams get a lightweight prompt-card format with purpose, inputs, prohibited data, expected output, test cases, owner, and last-review date.