Step 1
Collect the order identifiers, the address fields under review, the validation evidence for each field, the payment state and the handling rules the merchant applies.
Address validation returns mixed results, and some orders are held while others ship on the same kind of signal. In this case, the immediate task is to review order addresses for identifiable issues. The workflow should produce an exception queue that separates confirmed errors from uncertain signals and flags ambiguous fields for confirmation.
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 review order addresses for identifiable issues.
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: [order identifiers, the address fields under review, validation evidence for each field, payment state, and the merchant's handling rules]. Mark each order as a confirmed error or an uncertain signal from the evidence, never inferring a missing component, and send for customer confirmation any address whose fields disagree even where validation passed.
Return an order exception queue and resolution steps 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.
Collect the order identifiers, the address fields under review, the validation evidence for each field, the payment state and the handling rules the merchant applies.
Mark each order as a confirmed error or an uncertain signal from the evidence, never inferring a missing component, and send for customer confirmation any address whose fields disagree even where validation passed.
Deliver an exception queue naming the ambiguous parts and the resolution step for each, then verify the corrected values against the supplied records and note the date each case was cleared.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
A failed validation check, fields that disagree with each other, or a format the carrier will not accept for that destination. A pass returned with low confidence deserves the same review as an outright failure.
Only where reliable evidence supports it. An inferred house number or postal code can send the parcel to the wrong address, and the cost of correcting that exceeds the cost of asking.
When the order cannot proceed without confirmation and the delay affects the stated fulfillment promise. Contact only through approved channels and wording, and log what was confirmed.
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.