Expected output
The deliverable should include Gap map, Article outline, Standard answer, Metadata and tags, and Update ownership.
IntermediateCustomer Service & Retention AI
Customer Support Knowledge Base | Diagnostic Analysis Prompt
Customer Support Knowledge Base | Diagnostic Analysis Prompt is a copyable AI prompt for ecommerce sellers. Use it to build a searchable, maintainable, AI-ready knowledge base from tickets and policies from verified business inputs. Copy the full instruction, add your inputs and check the result before use.
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How the Customer Support Knowledge Base | Diagnostic Analysis Prompt works
Build a searchable, maintainable, AI-ready knowledge base from tickets and policies from verified business inputs. The prompt identifies data gaps first, ranks findings by evidence strength, and ends with phased actions that require human review.
Key instructions and output
The points below describe the task and expected output in this prompt.
Parameter guide
Prepare the task information listed below and replace placeholders with verified details from the actual case.
[High-volume tickets]
Replace this placeholder with verified, task-specific information before running the prompt.
[Existing articles]
Replace this placeholder with verified, task-specific information before running the prompt.
[Product, shipping, and return policies]
Replace this placeholder with verified, task-specific information before running the prompt.
[Search logs]
Replace this placeholder with verified, task-specific information before running the prompt.
[AI-unsolved cases]
Replace this placeholder with verified, task-specific information before running the prompt.
Copy the complete prompt
The complete source is shown below. Copy it from the top right to use it.
You are an ecommerce knowledge-operations lead. Your objective is to build a searchable, maintainable, AI-ready knowledge base from tickets and policies. Analyze the following real inputs: [High-volume tickets], [Existing articles], [Product, shipping, and return policies], [Search logs], [AI-unsolved cases]. 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) Gap map, Article outline, Standard answer, Metadata and tags, Update ownership; (4) executable actions for the next 7, 30, and 90 days; and (5) risks, counterexamples, and assumptions requiring human validation. Special requirement: Each article should resolve one clear intent. Policies must include region, version, and effective date.Test this prompt on a real task
Copy a starter instruction, add the required inputs, then run one example and review the output.
01Customer Support Knowledge Base | Diagnostic Analysis Prompt
The deliverable should include Gap map, Article outline, Standard answer, Metadata and tags, and Update ownership. Diagnose customer support knowledge base issues and prioritize evidence-backed action
Show prompt and variablesHide prompt and variables
Customer Support Knowledge Base | Diagnostic Analysis Prompt
The deliverable should include Gap map, Article outline, Standard answer, Metadata and tags, and Update ownership. Diagnose customer support knowledge base issues and prioritize evidence-backed action
You are an ecommerce knowledge-operations lead. Your objective is to build a searchable, maintainable, AI-ready knowledge base from tickets and policies. Analyze the following real inputs: [High-volume tickets], [Existing articles], [Product, shipping, and return policies], [Search logs], [AI-unsolved cases]. 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) Gap map, Article outline, Standard answer, Metadata and tags, Update ownership; (4) executable actions for the next 7, 30, and 90 days; and (5) risks, counterexamples, and assumptions requiring human validation. Special requirement: Each article should resolve one clear intent. Policies must include region, version, and effective date.Inputs to replace in this prompt
[High-volume tickets]- Replace this placeholder with verified, task-specific information before running the prompt.
[Existing articles]- Replace this placeholder with verified, task-specific information before running the prompt.
[Product, shipping, and return policies]- Replace this placeholder with verified, task-specific information before running the prompt.
[Search logs]- Replace this placeholder with verified, task-specific information before running the prompt.
[AI-unsolved cases]- Replace this placeholder with verified, task-specific information before running the prompt.
What to check before using the output
The generated result is a draft; check claims, numbers and operating conditions against source data before publishing, importing or acting on it.
Operating note 1
Replace every placeholder before running the prompt, and label key figures with their source, date range, and definition.
Operating note 2
Each article should resolve one clear intent. Policies must include region, version, and effective date.
Operating note 3
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.
Source & attribution
Compilation note and reference material
This section identifies the prompt’s creator and source, and marks details that could not be verified.
Vendolune
This Prompt was compiled by Vendolune from the scenario requirements and public reference material. The reference-page author is not credited as this Prompt's author.
Reference materialGorgias
This link is reference material and does not establish its page author as the author of this Prompt. The article provides prompts for analyzing low-CSAT causes, AI-to-human handoffs, customer friction, intents, and macro-coverage gaps from real tickets and support data.
The original page does not publish a rating, review, Product Hunt upvote, star, fork, install, or download count.
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