01
Step 1
Take the catalog export to audit, the rules to check in the order they must run, the audit frequency and trigger the merchant has stated, the owner assigned per failure type, and the record of the previous run.
Back to topic
Spark playbook scenarios · Spark: AI Store Assistant
Design recurring audits for product data quality
Product data drifts after every import, and one-off cleanups let the same errors return weeks later. In this case, the immediate task is to design recurring audits for product data quality. The workflow should produce a prioritized product issue report plus the schedule, naming the check order, the owner per failure type and the change since the last run.
Topic
Product Data Quality and Audits
Target keyword
shopify automated product audits
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 design recurring audits for product data quality.
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 catalog export to audit, the rules to check in their run order, the stated audit frequency and trigger, the owner assigned per failure type, and the record of the previous run]. Run the checks in the stated order, letting each rule pass or fail on the data alone, route every failure to its owner, and mark as needing human confirmation any check whose result depends on a judgment the record cannot settle.
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
Run the checks in the stated order, letting each rule pass or fail on the data alone, route every failure to its owner, and mark as needing human confirmation any check whose result depends on a judgment the record cannot settle.
03
Step 3
Deliver a prioritized product issue report plus the schedule itself, stating the check order, the owner per failure type and what changed since the last run, then compare the finding count per rule against the previous run so a rule that stopped working shows up as a drop to zero.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
What should trigger a run besides the schedule?
Any catalog import or bulk edit, since that is when drift enters. A schedule alone leaves new errors sitting until the next date, while a trigger checks them while the change is still fresh and the cause is still known.
How is a failing check kept from being ignored?
Give every failure type a named owner and re-report unresolved failures each run. Findings addressed to nobody stay open indefinitely, and repeating them shows whether an earlier fix actually held.
Why record findings per rule rather than per run?
A count that suddenly falls to zero usually means the rule stopped working, not that the catalog became clean. Per-rule counts make that difference visible before anyone reports the store as fixed.
Related scenarios
Continue with adjacent or same-topic scenarios
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
Run a repeatable product data audit
Diagnose incorrect or inconsistent displayed product prices
Assess listing completeness using explicit review criteria
Investigate missing or confusing variant information on a page
Identify missing or invalid variant prices
Detect repeated descriptions that obscure product differences
Next step
Want these AI scenarios embedded directly into daily operations?
Ciwi Spark builds Shopify-native AI tools and agents around the workflows merchants actually run: research, ads, translation, and customer support. Stop rewriting the same prompts by hand.