01
Step 1
Collect the languages the store answers in, the approved policy text in the source language, the existing translated versions with their last review dates, and past replies where versions diverged on the same question.
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Spark playbook scenarios · Spark: AI Store Assistant
Provide consistent support responses across languages
Each language falls to a different teammate, and answers about returns now read as three separate policies to anyone comparing them. In this case, the immediate task is to provide consistent support responses across languages. The workflow should produce sample ticket outcomes inside a support operating workflow compared version by version for divergence.
Topic
Customer Support Operations
Target keyword
shopify multilingual customer support
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 provide consistent support responses across languages.
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: [languages in use, approved source-language policy text, existing translated versions with review dates, and past replies where versions diverged].
Compare each translated version against the source clause by clause and classify every difference as a translation error, a locally required variation or a substantive policy change, keeping the first two with their basis recorded and returning the third to the policy owner.
Return sample ticket outcomes inside a support operating workflow across each language version 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
Compare each translated version against the source clause by clause and classify every difference as a translation error, a locally required variation or a substantive policy change, keeping the first two with their basis recorded and returning the third to the policy owner.
03
Step 3
Deliver the sample ticket outcomes inside the support workflow with each language version linked to the same source clause, then run a divergence check across versions and record which clauses still lack an approved translation.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
What counts as an acceptable local variation?
Only wording required by local law, tax rules or a delivery term that genuinely differs, recorded against the clause it varies; a variation changing who pays or when something happens is a policy change and needs approval as one.
Who settles a divergence between versions?
The policy owner decides the source meaning and the translator applies it, so the outcome does not depend on which language team noticed first; a local team may keep a legally required variant provided the clause reference is recorded.
How is a newly added language started?
It begins with no approved text and routes those requests to a person until each clause is translated and reviewed; machine translation may help draft a version but cannot serve as the approved wording a customer is answered with.
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