The short answer
Readers can distinguish the base model from the deployed system and avoid attributing every behavior to one mysterious “AI.”
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
A language model predicts and generates sequences from learned patterns, but the product people use also includes instructions, retrieval, tools, filters, memory, user data, and an interface.
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.
Simple metaphors help and can mislead. “Autocomplete” captures token prediction but not the full trained and deployed system.
A practical way to do it
- Identify the model, surrounding system, tools, and data sources.
- Ask which behavior comes from training versus runtime context.
- Treat confident output as a claim to verify, not evidence of understanding.
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 can distinguish the base model from the deployed system and avoid attributing every behavior to one mysterious “AI.”