Expected output
The deliverable should include Issue-by-issue table, Severity, Revision recommendations, Unverified items, and Publish recommendation.
Content Quality Assurance | Diagnostic Analysis Prompt is a copyable AI prompt for ecommerce sellers. Use it to check content for accuracy, completeness, consistency, readability, SEO, and compliance risks from verified business inputs. Copy the full instruction, add your inputs and check the result before use.
Check content for accuracy, completeness, consistency, readability, SEO, and compliance risks from verified business inputs. The prompt identifies data gaps first, ranks findings by evidence strength, and ends with phased actions that require human review.
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.
The complete source is shown below. Copy it from the top right to use it.
You are an ecommerce content QA and compliance editor. Your objective is to check content for accuracy, completeness, consistency, readability, SEO, and compliance risks. Analyze the following real inputs: [Content to review], [Product source of truth], [Brand guidelines], [Platform rules], [Regulations and prohibited terms]. First list data gaps and definitions that need confirmation. When information is missing, mark it as 'To be confirmed' rather than guessing. Then provide: (1) key findings and supporting evidence; (2) prioritized root causes or opportunities using impact × evidence strength; (3) Issue-by-issue table, Severity, Revision recommendations, Unverified items, Publish recommendation; (4) executable actions for the next 7, 30, and 90 days; and (5) risks, counterexamples, and assumptions requiring human validation. 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. Diagnose content quality assurance issues and prioritize evidence-backed action
You are an ecommerce content QA and compliance editor. Your objective is to check content for accuracy, completeness, consistency, readability, SEO, and compliance risks. Analyze the following real inputs: [Content to review], [Product source of truth], [Brand guidelines], [Platform rules], [Regulations and prohibited terms]. First list data gaps and definitions that need confirmation. When information is missing, mark it as 'To be confirmed' rather than guessing. Then provide: (1) key findings and supporting evidence; (2) prioritized root causes or opportunities using impact × evidence strength; (3) Issue-by-issue table, Severity, Revision recommendations, Unverified items, Publish recommendation; (4) executable actions for the next 7, 30, and 90 days; and (5) risks, counterexamples, and assumptions requiring human validation. Special requirement: Quote the exact text that triggers each issue. Factual inconsistencies outrank style issues, and all high-risk findings require human approval.[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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