01
Step 1
Collect each dispute record with its provider, case reference, event dates and response deadline, the matching order with its identifiers and amounts, and the evidence the merchant holds for that order.
Back to topic
Spark playbook scenarios · Spark: AI Store Assistant
Track dispute events and pending chargeback actions
Disputes arrive from several payment sources, each carrying its own response deadline, so cases sit unassigned until it is late. In this case, the immediate task is to track dispute events and pending chargeback actions. The output should be a queue ordered by deadline with the order evidence attached to each case.
Topic
Chargeback Alerts
Target keyword
shopify chargeback alerts
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 track dispute events and pending chargeback actions.
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: [dispute records with provider, case reference, event dates and response deadline, the matching order with identifiers and amounts, and the evidence the merchant holds for that order]. Order the cases by remaining time to the stated deadline, mark the pending action and its owner for each one, and flag any case whose deadline has already passed or whose order cannot be matched as needing immediate handling.
Return a dispute alert queue and evidence checklist 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
Order the cases by remaining time to the stated deadline, mark the pending action and its owner for each one, and flag any case whose deadline has already passed or whose order cannot be matched as needing immediate handling.
03
Step 3
Deliver the queue with the evidence checklist attached per case, mark each item present or missing against the merchant's own records, and verify every entry against the supplied dispute and order data without predicting an outcome.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
What puts a case at the top of the queue?
The least remaining time before its stated response deadline, so a case received later but due sooner outranks one with more time left regardless of the amount in dispute.
How is evidence matched to a case?
Through the order identifier on the dispute record, after which each required item is marked present or missing against the merchant's own records, so gaps surface before the deadline rather than after it.
Does the queue decide whether a dispute is won?
No. It makes deadlines and evidence gaps visible; the outcome rests with the provider once the submitted evidence is reviewed, so the output never states a likely result.
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.
Organize routine order handling using clear states
Detect payment failures that require follow-up
Build a prioritized store conversion improvement workflow
Respond to shipping inquiries using tracking evidence
Create a queue for orders requiring exception handling
Handle order-change requests within permitted actions
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.