Human-reviewed AI content
Why AI-drafted product content should never auto-approve
A review-first operating model that preserves source truth, uses confidence as attention guidance, and keeps provider capability separate from approval.
A practical guide for ecommerce, catalog, and product-content practitioners.
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Keep three layers of catalog truth
A source value, an AI-drafted suggestion, and an approved product attribute are not interchangeable. The source records what arrived, the suggestion records a proposed interpretation, and the approved attribute records a reviewed decision.
Collapsing those layers makes correction harder. A reviewer cannot tell whether a phrase came from the source, a rule, a model, or a previous edit. Keeping the layers distinct preserves accountability and the option to improve the AI method later.
Practice
- Never overwrite source content during suggestion generation.
- Store the prompt or profile version with the suggestion run.
- Promote values to canonical truth only through review.
“AI can prepare catalog decisions at scale; product truth changes only when a reviewer accepts or edits a suggestion.”
Use confidence to direct attention
Confidence is most useful as a triage signal. A low-confidence material or fit suggestion deserves closer inspection; a stronger color suggestion may need less time. Neither score authorizes a state change on its own.
Thresholds can order a queue or mark work as needing review, but they should not hide the evidence. Reviewers need the proposed value, source context, reason, and any brand rule that shaped the output.
Practice
- Sort uncertain or high-impact suggestions toward the top.
- Expose the evidence needed to make the decision.
- Avoid a global score that obscures field-level uncertainty.
Make review a durable decision
Accept, edit, and reject are different decisions. Accept preserves the proposed value, edit creates reviewer-authored truth, and reject records that the suggestion should not be used. Each action should be tenant-scoped and auditable.
Bulk review needs the same discipline. A filtered operation should report what matched, what changed, and what had already been reviewed. It should not describe a queued or partially applied operation as complete.
Practice
- Record actor, action, scope, and time for each decision.
- Keep reviewed suggestions immutable as evidence.
- Report bulk results without fabricating success.
Separate provider capability from product governance
Changing the AI provider should not change the approval contract. Different providers and model versions should all return the same strict suggestion shape and enter the same review path.
Provider availability is also not output quality. Workflow state and review controls cannot substitute for evaluating the suggestions themselves. Quality belongs in its own evidence and should not be inferred from a completed run.
Practice
- Validate provider output against a strict schema.
- Keep provider failures from creating fake suggestions.
- Evaluate model quality separately from workflow correctness.
Operating principles
What the method protects.
Review-first AI workflow
Tessaire stores strict-schema suggestions and supports accept, edit, reject, QA, and bulk-review decisions.
Provider identity stays visible
Each run keeps provider and model context attached so reviewers can distinguish the origin of every suggestion.
Approval is provider-independent
No provider or confidence score can promote a suggestion into canonical product truth automatically.
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