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Step 1
Start with product IDs, review or complaint evidence, sales exposure and issue classification rules.
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Spark playbook scenarios · Spark: AI Store Assistant
Surface recurring negative product review signals
Product complaints or quality signals accumulate without a clear review process. In this case, the immediate task is to surface recurring negative product review signals. The workflow should produce a product-risk review queue and alert conditions, using the merchant's actual records and constraints.
Topic
Product Quality and Review Alerts
Target keyword
shopify negative review alerts
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 surface recurring negative product review signals.
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 product IDs, review or complaint evidence, sales exposure and issue classification rules]. Group recurring signals, compare counts with exposure and create a prioritized investigation queue without treating allegations as verified defects.
Group recurring issues by product and topic, preserving the review context and avoiding conclusions from a single comment.
Return a product-risk review queue and alert conditions 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.
Product complaints or quality signals accumulate without a clear review process. In this case, the immediate task is to surface recurring negative product review signals. The workflow should produce a product-risk review queue and alert conditions, 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.
02
Step 2
Group recurring signals, compare counts with exposure and create a prioritized investigation queue without treating allegations as verified defects.
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Step 3
Group recurring issues by product and topic, preserving the review context and avoiding conclusions from a single comment.
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Step 4
Deliver a product-risk review queue and alert conditions; 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 one negative review establish a product defect?
No. It is a signal to investigate alongside repeated patterns and other evidence.
Why compare complaints with units sold?
Raw counts can overemphasize popular products. Exposure helps interpret how concentrated the issue may be.
Why group reviews by issue rather than sentiment alone?
Different complaints require different fixes. Topic patterns are more actionable than an overall positive or negative label.
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Next step
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