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Spark playbook scenarios · Spark: AI Store Assistant

Translate product information for a specified market

A new market is opening and the catalog must appear in its language, but no glossary or unit convention is agreed. In this case, the immediate task is to translate product information for a specified market. The workflow should produce localized copy shown beside the source, with a terminology list and every count or unit traceable to the merchant's records.

Topic
Product Translation and Localization
Target keyword
translate shopify product information
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 translate product information for a specified market.
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: [approved source copy per field, the target language and market, the glossary of locked terms, and local formatting conventions for units, dates and numerals].

Translate the requested fields while keeping locked terms, specifications and countable facts character-identical to the source, applying local unit and numeral formatting only where the convention does not change the recorded value.
Return localized copy beside the source with a terminology review list 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.
Use in Spark
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.

01

Step 1

Gather the approved source copy per field, the target language and market, the glossary with locked terms such as model codes, brand names and SKU identifiers, and the local formatting conventions for units, dates and numerals.

02

Step 2

Translate the requested fields while keeping locked terms, specifications and countable facts character-identical to the source, applying local unit and numeral formatting only where the convention does not change the recorded value.

03

Step 3

Deliver localized copy shown beside the source with a terminology review list of every ambiguous term, then verify field by field that locked terms are unchanged and that each count, unit and measurement traces to the merchant's record.

FAQ

Common questions when running this scenario

Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.

How are locked terms handled in output?

They are copied character by character rather than transliterated or adapted, and the field-level check compares each one against the glossary after translation; a locked term that differs even in case is treated as an error.

When may a unit be localized?

Only through a convention that preserves the recorded value, and the original unit stays visible in the terminology list; converting a measurement without merchant-approved conversion rules would quietly change the product fact.

What enters the terminology review list?

Every source term with more than one plausible reading, the chosen literal reading, and the reason; the list is the merchant's chance to catch an interpretation before the localized page goes live.

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.

Next step

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