Expected output
The deliverable should include Issue-by-issue table, Severity, Revision recommendations, Unverified items, and Publish recommendation.
Content Quality Assurance | Comparative Decision Prompt is a copyable AI prompt for ecommerce sellers. Use it to compare candidate options against one consistent decision framework. Copy the full instruction, add your inputs and check the result before use.
Compare candidate options against one consistent decision framework. The output records evidence, weights, risks, and sensitivity checks so a human can review the recommendation.
The points below describe the task and expected output in this prompt.
The deliverable should include Issue-by-issue table, Severity, Revision recommendations, Unverified items, and Publish recommendation.
Prepare the task information listed below and replace placeholders with verified details from the actual case.
Replace this placeholder with verified, task-specific information before running the prompt.
Replace this placeholder with verified, task-specific information before running the prompt.
Replace this placeholder with verified, task-specific information before running the prompt.
Replace this placeholder with verified, task-specific information before running the prompt.
Replace this placeholder with verified, task-specific information before running the prompt.
Replace this placeholder with verified, task-specific information before running the prompt.
Replace this placeholder with verified, task-specific information before running the prompt.
Replace this placeholder with verified, task-specific information before running the prompt.
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You are an ecommerce content QA and compliance editor. Compare [Option A], [Option B], and [Option C] for the objective: check content for accuracy, completeness, consistency, readability, SEO, and compliance risks. Inputs: [Content to review], [Product source of truth], [Brand guidelines], [Platform rules], [Regulations and prohibited terms]. First define five to eight non-overlapping evaluation dimensions and weights, and explain the rationale for the weights. Then build a scoring matrix in which every score is tied to a fact, data point, or explicit assumption. Provide the best choice, conditions under which it is best, irreversible risks, the lowest-cost validation method, and Issue-by-issue table, Severity, Revision recommendations, Unverified items, Publish recommendation. Do not invent missing information to force a conclusion. Special requirement: Quote the exact text that triggers each issue. Factual inconsistencies outrank style issues, and all high-risk findings require human approval.Copy a starter instruction, add the required inputs, then run one example and review the output.
The deliverable should include Issue-by-issue table, Severity, Revision recommendations, Unverified items, and Publish recommendation. Compare options and make a reviewable content quality assurance decision
You are an ecommerce content QA and compliance editor. Compare [Option A], [Option B], and [Option C] for the objective: check content for accuracy, completeness, consistency, readability, SEO, and compliance risks. Inputs: [Content to review], [Product source of truth], [Brand guidelines], [Platform rules], [Regulations and prohibited terms]. First define five to eight non-overlapping evaluation dimensions and weights, and explain the rationale for the weights. Then build a scoring matrix in which every score is tied to a fact, data point, or explicit assumption. Provide the best choice, conditions under which it is best, irreversible risks, the lowest-cost validation method, and Issue-by-issue table, Severity, Revision recommendations, Unverified items, Publish recommendation. Do not invent missing information to force a conclusion. Special requirement: Quote the exact text that triggers each issue. Factual inconsistencies outrank style issues, and all high-risk findings require human approval.[Option A][Option B][Option C][Content to review][Product source of truth][Brand guidelines][Platform rules][Regulations and prohibited terms]The generated result is a draft; check claims, numbers and operating conditions against source data before publishing, importing or acting on it.
Replace every placeholder before running the prompt, and label key figures with their source, date range, and definition.
Quote the exact text that triggers each issue. Factual inconsistencies outrank style issues, and all high-risk findings require human approval.
Before uploading order, customer, contract, or supplier data, redact sensitive information and comply with platform terms, privacy policies, NDAs, and company data-governance requirements. Have the responsible operator review the result before it is published, sent, or executed.
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