AI Product Descriptions: Prompt Pack + QA Checklist
A repeatable prompt pack plus a review rubric to keep accuracy and brand voice consistent at scale.
Why this matters
Product descriptions are a conversion surface and a support surface. When AI copy is inaccurate, you don’t just lose SEO—you create refunds, chargebacks, and “policy conflict” tickets (shipping/returns/warranty mismatch).
What “good” looks like
- Factuality: 0 invented specs; every claim maps to catalog data or policy text.
- Scanability: benefits in bullets, constraints in plain language, and a clear next step.
- Consistency: brand voice and policy wording stay stable across thousands of SKUs.
2 KPIs to track (start here)
- PDP → ATC rate (by template cohort) and PDP bounce (copy clarity proxy).
- Return reasons tied to “not as described” + support tickets mentioning specs/policies.
The goal of this page is to give you a repeatable prompt pack plus a QA rubric so AI accelerates production without creating downstream costs.
Framework / workflow
Think of AI copy as a pipeline: inputs → constraints → draft → verification → publish → measure. The highest leverage is not the model—it’s the input contract and the review gates.
Inputs you must provide (non‑negotiable)
- Catalog facts: title, variants, materials, dimensions, compatibility, care, included items.
- Policy snippets: shipping window, returns/eligibility, warranty terms, safety disclaimers.
- Voice samples: 2–3 “gold standard” PDPs (good tone, good structure).
Workflow (merchant-ready)
- Define the intent (one job per PDP): clarify, compare, reduce risk, upsell.
- Build a product brief (structured fields) and attach policy text. No brief → no generation.
- Generate 2 drafts (A = benefit-led, B = spec-led). Keep word-count bounds.
- Run QA gates (facts, policy alignment, prohibited claims, SEO sanity).
- Publish to a cohort (e.g., 50–200 SKUs) and monitor KPIs for 14–30 days.
- Iterate templates (not one-off edits). Update the prompt pack, not each product.
Guardrails (copy must NOT do)
- Invent certifications, performance guarantees, or medical/safety claims.
- Contradict your store policies (returns/warranty/shipping windows).
- Describe variants that don’t exist (colors, sizes, bundles).
- Over-optimize for keywords at the expense of clarity.
Templates / prompts
Use these prompts as stable templates. The objective is consistency at scale—same structure, same risk controls, and predictable review effort.
Template 1 — “Factual PDP draft” (default)
Role: You are an ecommerce copy editor for a Shopify store.
Goal: Write a product description that improves clarity and reduces returns.
Inputs (facts only):
- Product title:
- Category:
- Variants (size/color/pack):
- Materials:
- Dimensions/fit/care:
- Compatibility/what it's for:
- What's included:
Store policies (paste exact text):
- Shipping:
- Returns:
- Warranty:
Brand voice examples (paste 2 short excerpts):
- Example 1:
- Example 2:
Constraints:
- Use ONLY the provided facts and policy text. If a fact is missing, write "Unknown" and do NOT guess.
- No invented guarantees, certifications, or medical claims.
- Keep total length 140–220 words.
Output format:
1) One-sentence value proposition (<= 18 words)
2) 4–6 benefit bullets (plain language)
3) "Specs at a glance" (key facts; no fluff)
4) "Shipping / Returns / Warranty" (use policy wording; no paraphrase)
5) CTA line (single sentence)Template 2 — “Comparison + objection handling” (for high-consideration SKUs)
Role: You are a conversion copywriter.
Goal: Help shoppers choose confidently and reduce "not as described" returns.
Inputs: (same product facts + policy text as Template 1)
Optional: 2 competing alternatives in your catalog (names + key differences).
Constraints:
- Facts/policies only; do not invent. No superlatives unless supported by facts.
- Add a short "Who it's for / Not for" section.
- 180–260 words.
Output:
- Quick summary (1–2 lines)
- "Why you'll like it" (4 bullets)
- "Choose this if..." (3 bullets)
- "Not ideal if..." (2 bullets)
- Specs snapshot
- Policy block (exact text)Template 3 — “SEO-safe rewrite” (keep meaning, improve structure)
Role: You are an ecommerce editor.
Goal: Rewrite the description for clarity and scanability without changing facts.
Input:
- Existing description: (paste)
- Product facts: (paste)
- Policy text: (paste exact)
Constraints:
- Preserve factual meaning; do not add new claims.
- Keep primary keyword natural in the title + first paragraph.
- Output two versions: (A) bullet-led, (B) paragraph-led.Optional: Micro‑templates you can standardize
- Variant note: “Choose your size/color above. What’s shown may vary by variant.”
- Care line: “Care: [facts]. If unknown, omit.”
- Compatibility line: “Works with: [facts]. If unknown, omit.”
Execution layer: PDP content scoring
Before publishing AI product descriptions, score the page on factual accuracy, shopper usefulness, search alignment, and conversion clarity. A strong PDP does not merely sound better; it reduces hesitation.
- Use catalog facts and customer objections as inputs before asking AI to write.
- Separate benefits from claims: benefits can be persuasive, claims need proof.
- After publish, compare add-to-cart rate, search impressions, support questions, and returns for the edited products.
Checklist
Pre‑generation (data readiness)
- Catalog completeness: materials, dimensions/fit, care, compatibility, included items.
- Variant accuracy: titles/options match what’s actually purchasable.
- Policy text available: shipping/returns/warranty snippets ready to paste (exact wording).
Draft QA (publish gate)
- Fact check: every claim maps to data fields; no invented specs.
- Policy alignment: “Shipping / Returns / Warranty” uses exact store policy text.
- No risky claims: avoid medical/safety/certification/performance guarantees unless documented.
- Readability: clear bullets, avoids jargon, no wall-of-text.
- SEO sanity: primary keyword appears naturally (title + first paragraph). No stuffing.
- Internal links: include Shopify AI, Getting Started, and one of Tools or Use Cases.
Launch gates (recommended)
- Gate A: Publish 50–200 SKUs → monitor ATC, bounce, “not as described” returns for 14–30 days.
- Gate B: If metrics improve or hold steady, expand to 500–1,000 SKUs.
- Stop‑loss: Roll back the template if returns/tickets spike on the test cohort.
FAQ
How do I stop AI from inventing specs?
Treat missing data as a hard failure. Require a structured brief, and instruct the model to output Unknown for absent fields. Then fail QA if any “Unknown” appears in the final publish version.
Should I optimize for SEO keywords in every product description?
Only if it stays natural. For PDPs, prioritize clarity and purchase confidence. Use the primary keyword in the title + first paragraph, then focus on benefits, specs, and trust signals. Keyword stuffing increases bounce and reduces readability.
What’s the minimum QA I need before publishing at scale?
At minimum: (1) facts match catalog, (2) policy block uses exact policy text, (3) no prohibited claims, (4) variants not misrepresented, (5) readability check (bullets + plain language). Start with a 50–200 SKU cohort and iterate templates.
Do I need different templates for different categories?
Yes—use the same skeleton, but swap in category-specific sections (fit/care for apparel, compatibility for electronics, ingredients/allergens for consumables). Keep guardrails and policy alignment identical across categories.
How do I know this article is ready to publish?
When you’ve replaced drafts with real merchant examples, removed “Draft” notes, and validated that the templates reflect your actual catalog + policies. Then change robots to index,follow.
Start Shopify first, then add AI workflows where they’re measurable and safe.