01
Step 1
Load the product records under review with their identifiers, the required-field list, the format constraint attached to each field, and the publication status that decides whether a record is in scope.
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Spark playbook scenarios · Spark: AI Store Assistant
Validate product records against required data rules
Required fields are filled unevenly across new listings, and no single pass shows which records would fail publication. In this case, the immediate task is to validate product records against required data rules. The workflow should produce a prioritized product issue report listing required violations apart from optional warnings and citing the broken rule.
Topic
Product Data Quality and Audits
Target keyword
shopify product data validation
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 validate product records against required data rules.
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 product records with their identifiers, the required-field list, the format constraint attached to each field, and the publication status that sets the scope]. Test each field against its own constraint rather than judging the record as a whole, record required violations and optional warnings separately, and mark a record blocked once a required field fails while still checking its remaining fields so one pass exposes every fault.
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.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.
02
Step 2
Test each field against its own constraint rather than judging the record as a whole, record required violations and optional warnings separately, and mark a record blocked once a required field fails while still checking its remaining fields so one pass exposes every fault.
03
Step 3
Deliver a prioritized product issue report citing the rule each violation breaks and the field carrying it, then reconcile the blocked-record count against the records loaded and list any field with no stated constraint as an open rule gap.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
What happens to a record with several broken required fields?
Report every violation rather than only the first, so one pass yields a complete fix list. The record is blocked once, but each broken field needs its own correction before the record can pass a second run.
How are optional fields treated in validation?
They are checked but reported as warnings, never as blockers. An empty optional field lowers completeness without making the record unpublishable, which is exactly why the two lists must stay separate in the report.
What if a field has no rule attached?
Flag it as a rule gap and escalate it to the merchant instead of judging the values by eye. Applying an invented constraint produces findings nobody can confirm, and it quietly becomes the standard later.
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Continue with adjacent or same-topic scenarios
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Next step
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