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Spark playbook scenarios · Spark: AI Store Assistant

Audit a product page against the buyer's main questions

A page can look complete while still failing to answer the questions buyers actually hold before ordering. In this case, the immediate task is to audit a product page against the buyer's main questions. The workflow should produce an audit showing where each question is answered, buried or left unaddressed, alongside a recommended section order.

Topic
Product Page Audits and Layout
Target keyword
shopify product page audit
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 audit a product page against the buyer's main 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: [the live page in desktop and mobile states, buyer questions from support records, session device context, and available behaviour evidence]. Walk the buyer questions in their stated order and record for each one whether the page answers it directly, buries the answer below sections buyers skip, or stays silent, noting separately where the mobile order hides an answer the desktop layout shows.
Return an audit with a recommended section order and the reason for each move 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

Open the live page in both desktop and mobile states, and assemble the buyer questions from support records, the device context of most sessions, and whatever behaviour evidence the merchant can supply.

02

Step 2

Walk the buyer questions in their stated order and record for each one whether the page answers it directly, buries the answer below sections buyers skip, or stays silent, noting separately where the mobile order hides an answer the desktop layout shows.

03

Step 3

Deliver the audit with a recommended section order and the reason for every move, then verify each buyer question carries exactly one verdict and re-check the mobile reading so no question is left unclassified.

FAQ

Common questions when running this scenario

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

Where do the audit's buyer questions come from?

From the store's own support and pre-purchase contact records for that product, not a generic checklist, because only questions that block this product's purchase decision belong in the audit; each question is stated before the page is read.

Why audit mobile separately?

Because the mobile layout reorders sections and can hide an answer the desktop view shows clearly, the same question can hold different verdicts per device; the audit records desktop and mobile findings side by side instead of one blended verdict.

What does the recommended order rest on?

Each move is justified by the verdicts: answers to blocking questions move up, buried answers surface, and unanswered questions get a named section with the missing copy listed, so every placement change traces to a recorded finding.

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Next step

Want these AI scenarios embedded directly into daily operations?

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