Step 1
Assemble the policy documents with their version and effective date, order and fulfilment records, product and variant data, the shipping and return rules, and a sample of past tickets covering the questions agents repeat most.
Support agents answer the same questions repeatedly and the wording drifts from one shift to the next. In this case, the immediate task is to design grounded AI customer service responses. The workflow should produce a response set with source references and a routing rule for anything the available records do not cover.
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.
Act as a Shopify operations specialist.
Help me design grounded AI customer service responses.
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: [policy documents with version and effective date, order and fulfilment records, product and variant data, the shipping and return rules, and a sample of past tickets per recurring question].
Draft each response so every factual claim maps to one named source record, strip any statement the available records cannot carry such as delivery estimates, promised dates or goodwill exceptions, and attach an escalation path naming the destination queue and what the agent will need.
Return the response set with its source references reviewed against the merchant's stated policies 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.Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.
Assemble the policy documents with their version and effective date, order and fulfilment records, product and variant data, the shipping and return rules, and a sample of past tickets covering the questions agents repeat most.
Draft each response so every factual claim maps to one named source record, strip any statement the available records cannot carry such as delivery estimates, promised dates or goodwill exceptions, and attach an escalation path naming the destination queue and what the agent will need.
Deliver the response set with the source reference behind each answer, then walk it line by line against the merchant's stated policies and record every question left open because no approved record covers it.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Tickets are useful for spotting recurring questions, but the answer itself is rebuilt from the current policy version and product record, since an older reply may quote terms the merchant has since replaced.
They move to the escalation path with the queue and the details the agent needs rather than being answered by inference; an unsupported answer that later proves wrong costs more than a clean handover.
One named owner per policy area rechecks the set whenever terms, shipping rules or catalogue details change, recording the version reviewed and the next review date, so no reply outlives the policy it came from.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
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.