A request for an “AI-generated” article flag is one of today’s most discussed developer topics. A binary badge sounds simple but combines three different questions: who made the claims, how the prose was produced, and whether the result is verifiable.
Use an evidence ladder instead:
| Level | Required evidence |
|---|---|
| Claim | sources and dates for factual assertions |
| Reproduction | code, data, environment, expected result |
| Transformation | disclose translation, summarization, or generation assistance |
| Accountability | named human owner for corrections |
A platform can ask authors structured questions at publish time: Was generative AI used for prose? For code? Were reported tests actually executed? Which links are primary sources? The public UI should summarize the answers without turning disclosure into a warning label.
Measure whether the feature improves decisions: correction rate, source-link usage, successful reproduction, substantiated reports, and reader comprehension. Do not optimize for how many authors check a box.
Badges are easy to game when incentives reward the label rather than evidence. Random audits, edit history, and clear sanctions for fabricated tests matter more than guessing from writing style. Automated AI-text detectors should not be treated as proof.
The product goal is trustworthy technical knowledge. Generation method is relevant context, but evidence and accountability are what let a reader act safely.