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Spark playbook scenarios · Spark: AI Store Assistant

Summarize catalog quality issues by product and severity

Open catalog issues sit in several separate lists, so nobody can see which products carry the worst problems. In this case, the immediate task is to summarize catalog quality issues by product and severity. The workflow should produce a prioritized product issue report grouping issues by product, applying the severity levels in use and noting the evidence.

Topic
Product Data Quality and Audits
Target keyword
shopify product quality report
Copyable AI prompt

A prompt you can drop directly into Spark

First replace every [bracketed] placeholder with real store scope and constraints, then add any actual records or data you have, and paste it into your preferred AI assistant or Spark agent. Spark will follow this page's workflow to produce structured output and verification steps.

ai-prompt.txt
Act as a Shopify operations specialist.
Help me summarize catalog quality issues by product and severity.
My store context is [describe the business, products and target audience].

My scope is [specify the affected products, pages, customers or reporting period]. Use these inputs: [the open catalog issues with the product each one affects, the severity levels the merchant already uses and their definitions, and the closed issues to carry forward with status and close date]. Group the issues by product, apply the stated severity level without inventing a new one, count issues per product and per severity, and keep the evidence that ties each issue to that product.
Return a prioritized product issue report with evidence and suggested fixes for the specified task.
Show the relevant evidence or before-and-after examples and explain how I can verify the result.
If essential data is missing, ask for it and mark unsupported conclusions as unknown.
Do not invent metrics, product claims or customer facts.
For any publication, message, account or store change, use only connected tools within my explicitly authorized scope and report success only after verification; otherwise provide the draft or proposed steps.
Use in Spark
How to solve

A recommended resolution workflow

Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.

01

Step 1

Gather the open catalog issues with the product each one affects, the severity levels the merchant already uses and their definitions, and the closed issues to carry forward with their status and close date.

02

Step 2

Group the issues by product, apply the stated severity level without inventing a new one, count issues per product and per severity, and keep the evidence that ties each issue to that product.

03

Step 3

Deliver a prioritized product issue report with one row per product carrying its issue count, worst severity and suggested fix, then reconcile the grouped total against the issues supplied and list any issue whose product could not be identified.

FAQ

Common questions when running this scenario

Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.

How is a product ranked when it has many minor issues?

Rank by the worst severity first and by issue count second. Several small faults on one product still block less than a single severe one that stops a sale, and the report should say which drove the placement.

Should one issue affecting several products appear once?

List it under each affected product with a note that it is shared. Grouping it under a single product hides the work the others still need, and the shared cause then gets fixed in one place only.

Why keep closed issues in the report?

They show that a fault was real and was resolved, and they let the merchant spot a fix that did not hold. Dropping them makes every report start from zero with no record of what was settled.

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Next step

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