Structure product facts before generating copy

Specifications, materials, dimensions, compatibility, limitations and compliance statements must come from trusted data. AI can reshape the expression, but it cannot replace fact validation.

Create product master-data fields with evidence sources, and allow generation only from approved facts. Missing information should be flagged for review rather than guessed.

Break content work into reviewable steps

Titles, value propositions, long descriptions, search snippets and ad briefs serve different goals. Generating them in separate stages makes rules, tone and review standards clearer.

  • Fact check: use only approved fields
  • Brand voice: define preferred and prohibited language
  • Channel fit: follow length and policy constraints
  • Human review: keep a responsible approver before publishing
Key principle: define data ownership, direction and failure handling before writing integration code.

Keep versions and feedback data

Record input data, prompt template, model version, generated output, human edits and final published content so results remain explainable and improvable.

Evaluate search performance, conversion, return reasons and support questions after publication—not just whether the text sounds fluent.

What should not be fully automated

Health, safety, legal claims, certifications, compatibility and exact performance data require stricter human or professional review. Without a reliable source, the correct action is not to publish.