The short answer
Check every factual claim against the approved record for that exact product. Reject invented specifications, borrowed variant facts, and unsupported promises before judging the writing. Use a small set of known failure cases to test the workflow, then keep a human release decision and a recoverable prior version.
Give the writer a bounded job
Example brief: write 50–80 words for one product detail page using only the attached source record. Preserve units and limitations. Omit unknown claims and return a separate list of missing facts for review. The length is a house-style choice, not an SEO rule.
Shopify tells merchants to review AI suggestions before applying changes. Its product-description tool produces suggestions from the details provided. Review is part of using the output, not proof that generation was accurate.
Source: Shopify: Best practices for AI-powered tools; Shopify: Automatically generating product descriptions
Check the claims inside a fluent sentence
Fictional approved record: DEMO-SHELF-24, a 24-inch-wide powder-coated steel shelf; hardware included; indoor use only; load capacity and warranty not provided.
Draft to reject: “This rustproof shelf holds 100 pounds anywhere, backed by a lifetime warranty.” The source supports none of those promises. “Anywhere” also conflicts with the indoor-use limit.
A factual replacement fragment: “A 24-inch-wide powder-coated steel shelf for indoor use. Hardware is included.” The reviewer’s separate note asks for load capacity and warranty evidence. Missing facts stay missing; polished language does not fill them.
| Claim | Evidence | Verdict |
|---|---|---|
| 24-inch width | Exact product record | Supported |
| Hardware included | Exact product record | Supported |
| Rustproof / 100-pound capacity | Not provided | Reject |
| Use anywhere | Indoor use only | Reject |
| Lifetime warranty | Not provided | Reject |
Test the failures you would actually stop
Run these cases before adopting a new prompt, model, or source connector. Save inputs and full outputs, not just a pass rate. Anthropic’s evaluation guidance supports checking outcomes with code, model, and human graders suited to the task. The cases and release rules here are an original editorial adaptation.
| Case | Input condition | Required behavior |
|---|---|---|
| C1 · Complete source | All necessary facts supplied | Preserve facts and units |
| C2 · Missing capacity | No load rating | Omit capacity; flag missing evidence |
| C3 · Conflicting records | Two sources disagree on width | Hold for an owner decision |
| C4 · Sibling variant | Nearby record has a different finish | Use only the selected SKU’s facts |
| C5 · Expired promotion | Dated offer has ended | Do not repeat the offer |
| C6 · Instructions in source text | A product note tells the model to ignore the brief | Treat the note as data; preserve the approved task |
Separate factual acceptance from style
First record unsupported or contradicted claims, wrong variant facts, missing required warnings, and unwanted actions. Any such release blocker fails that output under this proposed rule. Then score clarity, usefulness, and brand tone. A strong style score cannot offset a false specification.
For a small pilot, run each of the six cases three times under unchanged settings: 18 outputs. Report how many outputs contain a blocker, which cases fail, and the review time. Eighteen outputs are a local regression check, not a reliability estimate for the whole catalog.
Use exact checks for identifiers, dates, and units; use a person for ambiguous meaning and unsupported implications. A model can help flag claims, but asking the same writer whether its own copy is correct is weak evidence. Recheck failed cases and affected neighbors after a repair.
Review the actual page before release
Save the source version, prompt, model identifier when available, output, edits, reviewer, and decision. Preview the approved text on the actual product page. Check that the selected variant and visible specifications still agree. Preserve the prior copy and the smallest useful rollback unit.
Measure accepted descriptions per review hour, including corrections and rework. Faster drafting alone does not establish lower production cost. If a workflow can also publish, keep that action separately authorized and verify what went live.
Everstead is my ongoing website and site-care project. Its public direction includes reviewing updates before publication. This product-copy scorecard is a proposed method; it is not a claim about an Everstead feature, customer result, or employer process.
Sources & scope
Primary references checked for this edition. The notes below distinguish source-backed facts from the frameworks and examples proposed in this guide.
- Shopify: Best practices for AI-powered tools ↗
Supports reviewing AI suggestions before applying changes. The shelf example is fictional and the scorecard is a proposed method.
Checked September 5, 2026 - Shopify: Automatically generating product descriptions ↗
Documents description suggestions generated from supplied product details, not guaranteed factual accuracy.
Checked September 5, 2026 - Anthropic: Demystifying evals for AI agents ↗
Supports task-specific evaluation, suitable graders, and inspecting outputs. The six cases, 18-run pilot, and release threshold are proposed choices, not a vendor standard.
Checked September 5, 2026
AI-assisted research and drafting. Provider-specific claims link to primary sources. Frameworks are editorial proposals; worked examples are illustrative and are not employer performance results.
Editorial policy & corrections ↗