AI FAQ Generation for Shopify: A Safe Workflow (With Templates)

Why this matters

FAQs are one of the few “AI wins” that compound: every accurate answer reduces repeat tickets, increases purchase confidence, and prevents returns. The failure mode is also common: AI invents policy, sizes, compatibility, or shipping promises—creating refunds and chargebacks.

• Primary KPI: ticket deflection rate for “pre-purchase” + “policy” intents (target a steady increase over 30–60 days).
• Quality KPI: “escalation after bot answer” rate (should fall as your FAQ gets tighter).
• Commercial KPI: conversion rate on PDP/collection pages where FAQ is present (track per template/page type).

Framework / workflow

Use a bounded workflow: facts → policy → brand voice. The model may draft wording, but it must never create new facts. Treat FAQ generation like a release process.

Step 0 — Define scope (where FAQs will live)

• PDP FAQ: sizing, materials, compatibility, care, warranty, “what’s included”.
• Collection FAQ: category guidance, filters/facets, “which one should I choose”.
• Policy FAQ: shipping windows, returns/exchanges, order changes, cancellations.

Step 1 — Build your inputs (the “source of truth” bundle)

Store these as a single input object per page type. If you can’t source it, the FAQ shouldn’t answer it.

• Catalog facts: title, variants, materials, dimensions, care, compatibility, “what’s in the box”.
• Policy snippets: returns/exchanges, shipping regions, processing time, warranties, subscriptions (copy/paste exact wording).
• Brand voice: 3–5 examples of “good answers” and “bad answers”.
• Constraints: what the model must refuse (medical claims, delivery guarantees, discounts, legal advice, unsupported countries).

Step 2 — Generate drafts with hard constraints

Generate question + short answer + escalation rule. Keep answers short. Longer explanations belong in guides, not FAQs.

Step 3 — Human QA (HITL) with a release checklist

• Fact check: every number, material, compatibility claim matches product data.
• Policy check: returns/shipping/warranty text matches your policy verbatim (no “creative paraphrase”).
• Risk check: no guarantees (“arrives in 2 days”), no medical/regulated claims, no pricing promises.
• SEO check: answer matches the query intent; no keyword stuffing.

Step 4 — Publish → measure → iterate

Track performance by intent buckets (shipping/returns, sizing, compatibility, order changes). Retire or rewrite the FAQs that drive escalations.

Templates / prompts

These templates are designed to prevent hallucinations. They force the model to cite inputs, refuse unknowns, and output in a consistent format.

Role: You are an ecommerce support writer.
Goal: Draft PDP FAQs that reduce pre-purchase questions without inventing facts.
Inputs:
- PRODUCT_FACTS: {title, variants, materials, dimensions, compatibility, what_in_box}
- POLICY_TEXT: {shipping, returns, warranty}
- VOICE_EXAMPLES: {good, bad}
Constraints:
- Use only PRODUCT_FACTS + POLICY_TEXT. If missing, answer: "We can't confirm — contact support."
- No delivery guarantees. No discounts. No medical/legal claims.
Output (JSON):
[
  {"q":"","a":"","evidence":["PRODUCT_FACTS.field","POLICY_TEXT.section"],"escalate_if":""}
]

Role: You rewrite policy into short FAQs.
Inputs:
- POLICY_TEXT: (paste exact policy wording)
Constraints:
- Do not change meaning; quote key numbers/limits exactly.
- If policy doesn't mention it, say: "Not covered in policy — contact support."
Output:
- 8 FAQs max
- Each answer ≤ 60 words
- Add "When to escalate" line per FAQ

Role: You are a product discovery assistant.
Inputs:
- COLLECTION_CONTEXT: {who_its_for, key_differences, top_filters}
- PRODUCT_GROUP_FACTS: (3–8 representative products with facts only)
Constraints:
- Recommend based on stated preferences; never invent performance claims.
Output:
- 5 FAQs focused on "which one should I choose", filters, and fit
- Each answer includes 2–3 decision bullets

Execution layer: FAQ source control

Checklist

Use this as your launch gate. If an item fails, the FAQ set doesn’t ship.

• Facts: materials, sizing, compatibility, “what’s included” match the catalog.
• Policy: shipping/returns/warranty statements match policy text verbatim.
• Scope: each FAQ maps to a known intent bucket; remove vanity questions.
• Refusal rules: unknowns explicitly escalate (no guessing).
• UX: answers are short; no walls of text; mobile friendly.
• Internal links: include Shopify AI, Getting Started, and one of Tools or Use Cases.

FAQ