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Spark playbook scenarios · Spark: AI Store Assistant

Draft grounded AI replies to support inquiries

Agents are trialling AI drafting, but generated answers have already quoted delivery dates that no order record could support. In this case, the immediate task is to draft grounded AI replies to support inquiries. The workflow should produce sample ticket outcomes inside a support operating workflow sourced only from approved policies and verifiable facts.

Topic
Customer Support Operations
Target keyword
shopify ai customer service replies
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 draft grounded AI replies to support inquiries.
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: [ticket examples, approved policy text per request type, and the order, payment and shipment fields the lookup can return].

Draft each reply only from the policy text and the looked-up record, quote the record value instead of restating it from memory, and where a needed fact is absent from the lookup, say what is confirmed and when the customer will hear back rather than estimating.
Return sample ticket outcomes inside a support operating workflow 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

Collect the ticket examples, the approved policy text each reply must stay inside, and the lookup available for order, payment and shipment facts, noting which fields that lookup can actually return.

02

Step 2

Draft each reply only from the policy text and the looked-up record, quote the record value instead of restating it from memory, and where a needed fact is absent from the lookup, say what is confirmed and when the customer will hear back rather than estimating.

03

Step 3

Deliver the sample ticket outcomes inside the support workflow with the source of every factual claim attached, then check each draft against that source and mark for human review any reply containing a claim no record supports.

FAQ

Common questions when running this scenario

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

How is a claim traced in a draft?

Every date, amount and status in the reply points to a field in the order or policy record, and the pointer is kept with the draft; a claim that cannot be traced is removed rather than softened, since a hedged wrong date is still a wrong date.

What should a draft do where policy is silent?

It stops and hands the case to a person, telling the customer a colleague will follow up; the drafting step may not compose a policy position, because an invented answer becomes the store's position the moment it is sent.

Which drafts need review even when facts check out?

Those touching refunds, address changes, cancellations or any commitment of money or timing, because the record confirms what happened, not what the store has agreed to do next; review applies per reply rather than once per template.

Related scenarios

Continue with adjacent or same-topic scenarios

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Next step

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