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Step 1
Start with page source or extracted structured data, visible content and validation messages.
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Spark playbook scenarios · Spark: AI Store Assistant
Validate supplied structured data and explain errors
Visible product information and machine-readable page data may be incomplete or inconsistent. In this case, the immediate task is to validate supplied structured data and explain errors. The workflow should produce a structured-data issue report and correction draft, using the merchant's actual records and constraints.
Topic
Structured Data and Schema
Target keyword
shopify structured data testing
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 validate supplied structured data and explain errors.
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: [provide page source or extracted structured data, visible content and validation messages].
Compare markup with visible facts, identify conflicting or missing fields and propose corrections that can be validated after implementation.
Return a structured-data issue report and correction draft 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.Scenario & problem
When does this scenario usually come up?
Start with the business context, the trigger, and the expected output before moving into the execution steps.
Visible product information and machine-readable page data may be incomplete or inconsistent. In this case, the immediate task is to validate supplied structured data and explain errors. The workflow should produce a structured-data issue report and correction draft, using the merchant's actual records and constraints.
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.
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Step 2
Compare markup with visible facts, identify conflicting or missing fields and propose corrections that can be validated after implementation.
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Step 3
Deliver a structured-data issue report and correction draft; verify the result against the supplied records and identify unresolved inputs.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Does valid structured data guarantee a special search appearance?
No. Validation checks technical consistency, but presentation in search results is determined separately.
Why must structured data match visible content?
Conflicting prices, availability or descriptions create contradictory signals and may misrepresent the page.
What information is needed to investigate this use case?
Start with page source or extracted structured data, visible content and validation messages. These inputs establish the relevant scope and help separate an actual issue from missing information or an unsuitable comparison. Without them, the result should remain a proposed approach rather than a confirmed diagnosis.
Related scenarios
Continue with adjacent or same-topic scenarios
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Next step
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