The products in this collection differ on a few attributes, yet the page presents them as one undifferentiated grid. In this case, the immediate task is to design a collection page that helps shoppers choose. The output should be a layout, grouping and introductory copy whose filters all map to attributes present in the merchant's product data.
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 design a collection page that helps shoppers choose.
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 collection's product list with populated option values, the attributes that vary across those products, available image aspect ratios, and the merchant's stated grouping rules]. Choose a layout and grouping relying only on attributes present in the records, drop every filter whose source attribute is blank for products it would hide, and treat an attribute filled on only some items as unfit for narrowing unless those missing values are shown as an explicit bucket.
Return a collection-page layout and copy brief checking that every proposed filter maps to real product data 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 collection's product list with every option value actually populated in the catalog, the attributes that genuinely vary across those products, the image aspect ratios available for each item, and the merchant's stated grouping or merchandising rules.
Choose a layout and grouping relying only on attributes present in the records, drop every filter whose source attribute is blank for products it would hide, and treat an attribute filled on only some items as unfit for narrowing unless those missing values are shown as an explicit bucket.
Deliver a layout and copy brief naming each proposed filter, the group order and the headline alongside the product data supporting it, check that every product can still be reached through at least one filter or group, and list any product that cannot.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Every product in the collection carries a value for that attribute. A filter built on a partly filled attribute silently removes suitable products from the results, which leaves the shopper with fewer choices than the page claims to hold.
As many as the differing attributes justify and no more, since each group should answer a decision the shopper actually makes. A collection varying on one attribute needs a sensible default sort rather than several rows of near-identical products.
A plain statement of what the assortment contains, who it suits and how the options differ. Everything drawn from attributes present in the product data, never claims about stock, price or delivery that the merchant's records do not carry.
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.