Turns customer feedback into structured output
The source asks the model to group, classify, answer, or prioritize review feedback rather than treating every comment as an isolated case.
Customer Review FAQ Prompt is a copyable AI prompt for Shopify, Amazon, WooCommerce sellers. Use it to turn customer review themes into draft product-page questions and answers. Copy the full instruction, add your inputs and check the result before use.
Use FAQ Generator from Customer Reviews to turn verified inputs into a first-pass product-content draft for human review. FAQ Generator from Customer Reviews fits Level 2: The mechanics are simple; good results depend on giving the model clean inputs and a clear review rule. The operator also needs enough store experience to judge the product-content draft against factual corrections, editing time, approval rate, and conversion quality.
This prompt asks the model to turn customer review themes into draft product-page questions and answers; the points below reflect instructions present in the source text.
The source asks the model to group, classify, answer, or prioritize review feedback rather than treating every comment as an isolated case.
Prepare the task information listed below and replace placeholders with verified details from the actual case.
Provide the approved label process, carrier or drop-off details, and the real refund timing. The source text can format these instructions but cannot look them up.
The complete source is shown below. Copy it from the top right to use it.
You are a product content strategist. I will provide customer reviews for a product. Generate a FAQ section based on real customer questions and concerns.
1. EXTRACT: Top 10 most common questions/concerns from reviews
2. GROUP: By category (sizing, usage, materials, shipping, returns, comparison, care)
3. WRITE FAQ: For each question:
- Natural question phrasing (how customers actually ask it)
- Clear, honest answer (50-100 words)
- When applicable: mention where to find more info on the product page
4. IDENTIFY OBJECTIONS: Top 3 reasons customers hesitated or returned
- Write FAQ entries that address these proactively
5. FORMAT: Q: [question]
A: [answer]
TONE: helpful, transparent, never salesy. If a product has a known limitation, address it honestly.Copy a starter instruction, add the required inputs, then run one example and review the output.
Do not begin with a store-wide rollout. Pick one reversible task where FAQ Generator from Customer Reviews can help you create, localize, or check product content and visual assets. Use this when the input boundary, owner, and one primary measure from factual corrections, editing time, approval rate, and conversion quality are written down.
Use the Prompt above to help me with this task: Start with one real task.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Do not begin with a store-wide rollout. Pick one reversible task where FAQ Generator from Customer Reviews can help you create, localize, or check product content and visual assets.
Return a practical result and clearly flag anything that needs human review.[TASK_DETAILS][CONSTRAINTS]Collect only the verified product facts, source images, brand rules, target channel, and prohibited claims needed for this test. Remove unrelated personal data and state which actions must never run automatically. Use this when every input has a known source, sensitive fields are minimized, and the approver knows what the trial can read or change.
Use the Prompt above to help me with this task: Prepare the input and guardrails.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Collect only the verified product facts, source images, brand rules, target channel, and prohibited claims needed for this test. Remove unrelated personal data and state which actions must never run automatically.
Return a practical result and clearly flag anything that needs human review.[TASK_DETAILS][CONSTRAINTS]Replace the placeholders in FAQ Generator from Customer Reviews with verified business information. Run one normal example, then one example with a missing field or edge case. Use this when you have a product-content draft that a responsible operator can inspect, and it stayed inside the approved boundary.
Use the Prompt above to help me with this task: Replace the placeholders and run the Prompt.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Replace the placeholders in FAQ Generator from Customer Reviews with verified business information. Run one normal example, then one example with a missing field or edge case.
Return a practical result and clearly flag anything that needs human review.[TASK_DETAILS][CONSTRAINTS]Do not judge the result by fluency. Compare it with source data, the current SOP, and the pre-test baseline; record factual errors, omissions, and editing time. Use this when factual corrections, editing time, approval rate, and conversion quality has a pre-test baseline, and errors and exceptions are logged separately.
Use the Prompt above to help me with this task: Review it against a baseline.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Do not judge the result by fluency. Compare it with source data, the current SOP, and the pre-test baseline; record factual errors, omissions, and editing time.
Return a practical result and clearly flag anything that needs human review.[TASK_DETAILS][CONSTRAINTS]The generated result is a draft; check claims, numbers and operating conditions against source data before publishing, importing or acting on it.
The instruction includes example eligibility and resolution paths. Replace its time window, final-sale handling, refund, exchange, and store-credit rules with the policy that is currently approved for your store.
The workflow may need an order reference or case facts. Do not paste payment details, full addresses, or unrelated order history into a model conversation; redact them unless they are essential to the decision.
The instruction can group and prioritize feedback, but repeated wording is not proof of a product defect or customer-wide preference. Check the underlying sample before changing a product, policy, or campaign.
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