The short answer
Readers get a diagnostic table linking failure types to source checks, retrieval changes, prompt constraints, tool use, refusal, and human review.
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
An answer can invent a fact, misquote a source, follow a false premise, confuse entities, use stale information, or express uncertainty poorly. Each failure needs a different fix.
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 more cautious tone can hide an error, while a confident tone can accompany a correct answer. Calibration and factuality are related but distinct.
A practical way to do it
- Name the exact unsupported or contradicted claim.
- Trace input, retrieval, tool output, and generation separately.
- Test whether the proposed mitigation reduces that failure on held-out cases.
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
Readers get a diagnostic table linking failure types to source checks, retrieval changes, prompt constraints, tool use, refusal, and human review.