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Step 1
Start with product IDs, current attributes, authoritative supplier material and desired units or field schema.
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Spark playbook scenarios · Spark: AI Store Assistant
Convert product measurements while preserving meaning
Important specifications or attributes are absent or inconsistent across supplier records. In this case, the immediate task is to convert product measurements while preserving meaning. The workflow should produce an enriched product data draft with unresolved fields, using the merchant's actual records and constraints.
Topic
Product Data Enrichment
Target keyword
shopify product unit conversion
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 convert product measurements while preserving meaning.
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: [provide product IDs, current attributes, authoritative supplier material and desired units or field schema]. Match source facts to products, normalize units and formats and flag uncertain matches instead of filling them with guesses.
Return an enriched product data draft with unresolved fields 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.Scenario & problem
When does this scenario usually come up?
Start with the business context, the trigger, and the expected output before moving into the execution steps.
Important specifications or attributes are absent or inconsistent across supplier records. In this case, the immediate task is to convert product measurements while preserving meaning. The workflow should produce an enriched product data draft with unresolved fields, using the merchant's actual records and constraints.
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
Match source facts to products, normalize units and formats and flag uncertain matches instead of filling them with guesses.
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Step 3
Deliver an enriched product data draft with unresolved fields; verify the result against the supplied records and identify unresolved inputs.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Why does consistent unit formatting matter?
Inconsistent units make comparison difficult and can create incorrect expectations about size or quantity.
What if two suppliers give conflicting specifications?
Keep both sources visible and request confirmation. Do not silently choose a value just to complete the record.
What information is needed to investigate this use case?
Start with product IDs, current attributes, authoritative supplier material and desired units or field schema. These inputs establish the relevant scope and help separate an actual issue from missing information or an unsuitable comparison. Without them, the result should remain a proposed approach rather than a confirmed diagnosis.
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Next step
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