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Spark playbook scenarios · Spark: AI Store Assistant

Identify and fill supported missing product fields

Listings are live with required fields left blank, and supplier material exists for some of these products. In this case, the immediate task is to identify and fill supported missing product fields. The workflow should produce an enriched product data draft with unresolved fields, each entry verified against the merchant's own records.

Topic
Product Data Enrichment
Target keyword
shopify missing product fields
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 identify and fill supported missing product fields.
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 identifiers in scope, the per-category required field list, the population status of every field today, and the supplier or merchant material available per product]. Rank the open fields by how directly they block a purchase — purchasable price, variant detail bearing on availability, and the attributes buyers compare before choosing — then fill only values a named source states for that particular product, recording the source and date, and leaving every gap without a source unresolved instead of borrowing from another product.
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.
Use in Spark
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.

01

Step 1

Collect the product identifiers in scope, the fields required for each category, the current population status of every one of them, and the supplier or merchant material available to close each gap.

02

Step 2

Rank the open fields by how directly they block a purchase — purchasable price, variant detail bearing on availability, and the attributes buyers compare before choosing — then fill only values a named source states for that particular product, recording the source and date, and leaving every gap without a source unresolved instead of borrowing from another product.

03

Step 3

Deliver an enriched draft with an unresolved list naming product, field, why it is open and who can close it, then reconcile filled and unresolved entries against the opening gap count and record which fields were closed and from which source so progress between runs is measurable.

FAQ

Common questions when running this scenario

Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.

Which gaps should be closed first?

Those blocking a purchase outright: price, variant detail affecting availability, and the attributes buyers weigh between alternatives. Optional descriptive attributes can wait, so ranking follows the decision the shopper is trying to make rather than how easy the missing value is to obtain.

Can a value be taken from a similar product?

No. A sibling product may differ in material or tolerance while looking identical, and a copied value becomes indistinguishable from a verified one once written. Products with no source for a field stay unresolved with the request noted, keeping the gap visible instead of quietly buried.

How is progress tracked between runs?

Each run records the fields closed, the source and date behind every value used, and the gaps still open with their owner. Comparing records run to run shows whether the catalog is genuinely completing or the same questions keep reopening, and tells the merchant exactly whom to chase.

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Next step

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