01
Step 1
Gather the recurring pre-purchase questions from support tickets, reviews and return reasons within a merchant-defined lookback window, ranked by frequency, together with each product's verified attributes.
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Spark playbook scenarios · Spark: AI Store Assistant
Generate product descriptions that answer buyer questions
Support tickets and reviews keep repeating the same pre-purchase questions, but the listings never address them directly. In this case, the immediate task is to generate product descriptions that answer buyer questions. The workflow should produce descriptions in the order buyers ask, each answer tied to a verified attribute in the merchant's records.
Topic
Product Titles and Descriptions
Target keyword
shopify product description generator
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 generate product descriptions that answer buyer questions.
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: [recurring pre-purchase questions from tickets, reviews and return reasons within the lookback window, ranked by frequency, with each product's verified attributes]. Structure each description so the highest-ranked questions are answered first in the order buyers ask them, tie every answer to a specific recorded attribute, and route questions about policy rather than product to the relevant store page.
Return descriptions mapped to those questions and confirm every answer against the supplied attributes 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.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
Structure each description so the highest-ranked questions are answered first in the order buyers ask them, tie every answer to a specific recorded attribute, and route questions about policy rather than product to the relevant store page.
03
Step 3
Deliver descriptions mapped question-by-answer with the attribute each answer cites, then verify that every recurring question in the window is either answered, linked to a policy page, or flagged as unanswerable due to a product data gap.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
How are questions ranked for the description?
Questions are counted across tickets, reviews and return reasons inside the merchant-defined window and answered in frequency order, so the most common misunderstanding is addressed first; one-off questions are linked or dropped rather than given equal space.
Why must each answer cite an attribute?
An answer tied to a recorded attribute can be re-verified whenever the product changes, while unsupported phrasing drifts into a claim the merchant never approved; the citation list is what makes the description maintainable.
What happens to questions the records cannot answer?
They are flagged as product data gaps in the delivery rather than written around, because a question buyers keep asking and the page keeps dodging is a returns risk, not a copywriting problem.
Related scenarios
Continue with adjacent or same-topic scenarios
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
Draft clearer product copy for a defined audience
Generate descriptions for a batch of product records
Generate clear product titles from verified attributes
Rewrite existing descriptions without changing product facts
Improve product titles for clarity and search relevance
Prepare a batch of revised product titles
Next step
Want these AI scenarios embedded directly into daily operations?
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.