The short answer
Readers can ask where their data goes, what changes persist, and which mechanism is responsible for a product feature.
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
Training learns model parameters, inference generates an output, fine-tuning further adjusts a model, and retrieval supplies outside information at runtime. Mixing the terms causes bad privacy and capability assumptions.
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.
“Your data is not used for training” does not by itself answer retention, review, logging, retrieval indexing, or subprocesser questions.
A practical way to do it
- Ask which data flows occur at setup and at each request.
- Identify what is stored in parameters, indexes, logs, or account history.
- Verify deletion and model-improvement settings separately.
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 ask where their data goes, what changes persist, and which mechanism is responsible for a product feature.