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Step 1
Start with catalog IDs, existing tags and product types, approved taxonomy and rules that depend on them.
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Spark playbook scenarios · Spark: AI Store Assistant
Classify products using an approved catalog taxonomy
A growing catalog uses inconsistent labels, making filtering and downstream rules unreliable. In this case, the immediate task is to classify products using an approved catalog taxonomy. The workflow should produce a product-level tagging and categorization change list, using the merchant's actual records and constraints.
Topic
Product Tags and Categorization
Target keyword
automatically categorize shopify products
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 classify products using an approved catalog taxonomy.
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 catalog IDs, existing tags and product types, approved taxonomy and rules that depend on them]. Normalize synonyms, map products to approved labels and generate explicit additions or removals while identifying dependent filters and workflows.
Return a product-level tagging and categorization change 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.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.
A growing catalog uses inconsistent labels, making filtering and downstream rules unreliable. In this case, the immediate task is to classify products using an approved catalog taxonomy. The workflow should produce a product-level tagging and categorization change list, 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
Normalize synonyms, map products to approved labels and generate explicit additions or removals while identifying dependent filters and workflows.
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Step 3
Deliver a product-level tagging and categorization change list; 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.
Are tags and product categories interchangeable?
They can serve different organizational purposes. Define which field each workflow uses instead of assuming one label controls every storefront or reporting feature.
Why avoid creating a new tag for every wording variation?
Uncontrolled synonyms fragment filters and reports. A maintained vocabulary keeps equivalent products grouped consistently.
What information is needed to investigate this use case?
Start with catalog IDs, existing tags and product types, approved taxonomy and rules that depend on them. 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.
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.
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Next step
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