Products are shown side by side, but each listing describes different attributes, so buyers cannot compare them on the same basis. In this case, the immediate task is to compare products using consistent attributes. The workflow should produce a comparison table and guided selection explanation, with each attribute checked against the merchant's own records.
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 compare products using consistent attributes.
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: [the products in scope, the merchant's attribute schema, each attribute's recorded value per product, the required-versus-optional marking per category, and the stated buyer priorities].
Build the table with one product per row and each attribute in a fixed column using a single unit and a single naming form throughout, leaving cells blank and marked as not stated where the merchant's records hold no supported value, and flagging products missing an attribute marked required for their category.
Return a comparison table and guided selection explanation 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 products to place side by side, the attribute schema the merchant uses, the recorded value of each attribute wherever one exists, and the buyer priorities named for this range, marking every attribute as required or optional for the category in question.
Build the table with one product per row and each attribute in a fixed column using a single unit and a single naming form throughout, leaving cells blank and marked as not stated where the merchant's records hold no supported value, and flagging products missing an attribute marked required for their category.
Deliver the comparison table with a guided note explaining which attributes decide the choice between these products and why, then verify the table covers every product in scope, that each value traces to the merchant's own records, and that no cell carries a figure copied or inferred from another product.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
They must share enough attributes with recorded values for a buyer to weigh them on the same terms; two columns full of blanks teach nothing. Products missing required attributes for their category still appear, flagged rather than dropped, because an honest gap tells the shopper something no filled column could convey.
Each column carries one unit and every value is restated into it, with the original figure retained for checking. Where restating depends on an assumption about the product's form, the cell stays unresolved, because once published a converted guess looks exactly like a measured value and there is no telling them apart.
The merchant's stated buyer priorities, so the attribute that actually settles this purchase leads the table. Price comes first only where the merchant names it as the deciding factor; treated as just another attribute elsewhere, it stops steering every shopper toward the cheapest row by default.
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.