A shopper is choosing inside a product with several tiers, and the merchant wants the upgrade offered only where it is a real step up. In this case, the immediate task is to recommend appropriate product upgrades. The workflow should produce a recommendation matrix and offer copy naming the variant, price and stock record behind each upgrade rule.
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 recommend appropriate product upgrades.
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 product's variant and tier structure with its option values, the price and inventory state of each tier, the differences recorded between tiers such as capacity, materials or included items, and the tier the shopper has currently selected]. Offer the next tier only where it adds a difference the shopper plausibly needs, its inventory records show it can be supplied and the price step is one the merchant has accepted, suppress it where the higher tier only raises the price with no recorded difference, and never present a tier the records show as unavailable.
Return a recommendation matrix and offer copy, listing the rules and the records behind each one 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.
Gather the product's variant and tier structure with the option values behind it, the price and inventory state of each tier, the differences recorded between tiers such as capacity, materials or included items, and the tier the shopper has currently selected.
Offer the next tier only where it adds a difference the shopper plausibly needs, its inventory records show it can be supplied and the price step is one the merchant has accepted, suppress it where the higher tier only raises the price with no recorded difference, and never present a tier the records show as unavailable.
Deliver a recommendation matrix and offer copy naming the variant, price and stock record behind each upgrade rule, then confirm that each recommended tier resolves to a live variant, that the originally selected tier stays selectable, and that every suppressed rule carries the reason it was held back.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
No. The rule offers the next tier that adds a difference the shopper plausibly needs, because the top tier is often a price jump the shopper did not come for and can stall the purchase. The highest tier still appears in the matrix with the reason it was skipped, so the choice stays visible.
The rule is suppressed and recorded with the inventory record behind that decision, and no tier the merchant did not name is substituted. Where a lower tier is available and still a real step up, it can be offered under its own rule, but an unavailable tier is never shown with a promise to restock.
No. It is presented alongside the selected tier, which stays selectable, so the shopper keeps control and the change list records both options. Replacing the choice turns a suggestion into a substitution, and the merchant loses the ability to tell whether the upgrade was wanted or merely accepted 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.