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Spark playbook scenarios · Spark: AI Store Assistant

Prepare product pages for a catalog batch

A catalog batch has to go live together, and one page template rarely fits every record inside it. In this case, the immediate task is to prepare product pages for a catalog batch. The workflow should produce the batch of page drafts plus a per-product exception list naming the records that fell outside the common template.

Topic
Product Page Creation
Target keyword
shopify bulk create product pages
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 prepare product pages for a catalog batch.
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: [batch product records with identifiers and specifications, image references, shared policy wording, and the exported record count for reconciliation]. Apply one section structure across the batch, flag every record whose required fields are missing or whose data does not fit the shared template, and route flagged records to the exception list with the specific field named.
Return the batch of page drafts with a per-product exception list, verified against the supplied records 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

Export the batch's product records with their identifiers, specifications, image references and shared policy wording, and record the exported count so the delivered total can be reconciled against it.

02

Step 2

Apply one section structure across the batch, flag every record whose required fields are missing or whose data does not fit the shared template, and route flagged records to the exception list with the specific field named.

03

Step 3

Deliver the batch of drafts plus a per-product exception list naming each missing or non-conforming field, then verify that the draft count plus the exception count equals the exported record count and that no draft carries an unfilled required field.

FAQ

Common questions when running this scenario

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

Should a batch share one page structure?

Yes for drafting efficiency, provided the shared structure has a slot for each product's unique specifications. Products whose decision factors differ enough that the template buries them move to the exception list instead of being forced into generic copy.

How are exceptions handled in a batch?

They are pulled into a separate list naming the missing or non-conforming field, so the rest of the batch proceeds to review without waiting. Each exception stays out of the publish queue until its named field is supplied and checked.

Is a batch draft ready to publish?

No. Each draft is reviewed against its own record, its required assets confirmed attached, and the count check passed first. Publishing straight from a batch draft risks carrying one unresolved exception live alongside everything else.

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Next step

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