The fastest reliable method is facts first, then voice, then platform tuning: verify every spec against the product record, rewrite for benefits and brand tone, then format for the platform and run one or two targeted prompts to fix what’s left. A tool like MerchUp speeds the drafting, but a human still checks the facts. Budget a few minutes per SKU, not hours.
TL;DR:
- Verify all product facts against official specifications before rewriting to prevent AI hallucinations and ensure accuracy.
- Replace vague adjectives with measurable proof points like material details or performance metrics to communicate real benefits.
- Format descriptions for each platform, balancing keyword placement, concise structure, and accessibility, without sacrificing clarity.
- Use targeted prompts and side-by-side comparison tools to refine tone, improve phrasing, and make edits more efficient through iterative prompts.
- Prioritize high-traffic SKUs for full fact-checking and detailed rewrites, applying lighter review to lower-volume products to optimize resource use.
How do you edit AI generated descriptions quickly?
Editing AI generated descriptions works best as a fixed sequence, not a scattershot polish. Run the same five steps on every SKU and the process gets faster each time you repeat it.
- Verify core facts and specs. Check every claim against the product record or spec sheet: dimensions, materials, compatibility, care instructions. If the AI invented a detail you can’t confirm, cut it.
- Strip generic adjectives. Words like “premium”, “amazing”, or “high-quality” carry no information. Replace each with a measurable proof point: a fabric weight, a battery life, a test result.
- Format for the platform. A Shopify product page wants a headline, three to five bullets, a short paragraph, and a clear call to action. A marketplace listing wants something tighter.
- Add the keyword and one high-intent variant. Work the primary search term into the headline or first sentence naturally, then a related phrase into a bullet or the meta description.
- Run final checks. Confirm readability (short sentences, no jargon), accessibility (alt text, no colour-only cues), compliance (no unverified health or safety claims), and that every measurement uses the correct unit.
That sequence covers most of what a listing needs before it can go live.
Pro Tip: Keep a running spec sheet per product category (apparel, electronics, homeware) so step one takes seconds instead of minutes once you’ve built it.
What should you check line by line in an AI draft?
Speed matters, but a rushed edit lets bad claims slip through. Go sentence by sentence and test each one against four questions: is it true, is it useful, does it sound like you, and does it help the listing get found?
Factual accuracy comes first. If a claim can’t be confirmed against a spec sheet, product manual, or supplier data, either verify it or delete it. Google’s own guidance on AI-generated business descriptions is blunt about this: the Google Business Profile description tool explicitly tells users to review AI drafts for accuracy before publishing, because the model can produce plausible-sounding details that aren’t correct. Treat any unverifiable statement as a red flag, not a minor style issue.
Benefit translation comes next. AI models tend to list features and stop there. “Stainless steel construction” is a feature. “Won’t rust, even left outdoors through winter” is a benefit. Rewrite each sentence to answer the buyer’s real question: what does this feature actually do for me?
Voice and tone need a separate pass. Read the paragraph against three or four sentences from your actual brand copy. Does it match the sentence rhythm, the level of formality, the humour (or lack of it)? If not, note the gap rather than guessing at a fix.
SEO placement is a quick structural check:
- Primary keyword in the headline or first sentence.
- One related variant in a bullet.
- Meta description under roughly 155 characters, with the keyword near the start.
- Schema-friendly details (brand, material, size) stated in plain text somewhere in the body.
Red flags to remove outright: hallucinated certifications, invented awards, specific numbers with no source, and any medical, safety, or “clinically proven” language that hasn’t been checked by someone qualified to make that claim. A survey of AI product-copy problems consistently points to the same failure mode: fluent, confident sentences describing things that never happened. That confidence is exactly what makes hallucinated specs dangerous. A model doesn’t hedge when it’s wrong; it writes the fake certification with the same certainty as the real dimensions.
How do you build a workflow with iterative AI prompts?
Editing AI generated text works better as a loop than a one-shot rewrite. Lock your facts, then use the model to refine tone and phrasing in small, targeted passes rather than asking for a whole new draft each time.
- Build a facts sheet first. List confirmed specs, dimensions, materials, and compatibility details before you touch the description. This becomes your ground truth for every prompt that follows.
- Write one targeted prompt per problem. Instead of “make this better”, try: “Shorten this to under 150 characters, keep the compatibility line.” Or: “Rewrite the second sentence to focus on durability, and include a measurable proof point.” Specific, one-line instructions consistently outperform vague requests when guiding a model toward a desired output.
- Use a side-by-side diff where the tool supports it. Many AI editing interfaces highlight additions and removals so you can accept changes line by line rather than swallowing an entire rewrite.
- Regenerate only the weak line. If the opening hook is flat but the bullets are fine, ask the model to rewrite just the opening. Keep everything that already works.
- Run final QA. Check readability, confirm any schema-relevant details are intact, check the platform’s character limits, and flag the listing for an A/B test if it’s a high-traffic SKU.
Pro Tip: Save your best prompts as reusable snippets (“tone: confident but not salesy”, “length: under 300 characters”) so you’re not rewriting instructions from scratch every time.
Tools built for this kind of rephrasing, including Grammarly’s humaniser, can also smooth AI phrasing into something that reads more like a person wrote it, though they don’t check whether the underlying facts are correct. That verification step stays with you.
What do good before-and-after edits actually look like?
Structure changes by platform. A Shopify product page can carry a full headline, four or five bullets, and a two or three sentence paragraph. A marketplace short blurb often caps out around 200 characters. Ad copy needs the whole pitch in one line.
Category details matter more than generic polish:
- Apparel: always state fit (“runs small, size up”) when that data exists. Fit guidance reduces returns more reliably than stylistic rewrites.
- Electronics: state compatibility explicitly (supported OS, included cables, model numbers). Vague compatibility language is one of the most common causes of negative reviews.
- Homeware: lead with material and care instructions, since those drive both purchase decisions and return rates.
Three quick examples:
Before: “This premium water bottle keeps drinks cold for a long time.” After: “Double-walled stainless steel keeps drinks cold for 24 hours, hot for 12. Fits standard car cup holders.”
Before: “High-quality cotton t-shirt, comfortable fit.” Runs slightly small, order one size up."
Before: “Wireless earbuds with great sound and long battery life.” After: “Bluetooth 5.3 earbuds, 8-hour battery per charge, compatible with iOS and Android. USB-C cable included.”
The pattern across every fix is the same: swap the vague claim for the number, the material, or the compatibility detail a buyer actually needs before checkout.
Keep the call to action short and mobile-first. “Add to cart” beats a paragraph explaining why they should.
Where MerchUp fits into this editing process
MerchUp was built around the exact sequence described above: verify, rewrite, format, iterate. It ingests product specs directly, applies drag-and-drop templates for the bullet-and-headline structure most platforms expect, and pushes bulk edits straight into a live Shopify catalogue.
- Spec ingestion means the facts sheet step happens automatically rather than manually per SKU.
- Bulk editing tools let you apply a tone or length fix across hundreds of listings at once, instead of opening each one.
- Templates and a visual editor handle the platform-formatting step: headline, bullets, short paragraph, CTA.
- Live Shopify integration publishes the edited version without a separate export step.
This is the article’s guidance from Jamie Moss, writing on how these workflows scale for merch teams managing large catalogues.
One thing worth being honest about: MerchUp speeds drafting and bulk editing, but it doesn’t replace the human fact-check. Nobody’s tool does. Whatever generates the first draft, someone still has to confirm the dimensions are right.
What actually matters when you’re editing at scale
Not every SKU deserves the same attention. Prioritise by revenue and traffic first. The long tail can go through a lighter checklist, spot-checked rather than fully rewritten.
Set a pass/fail rule before you publish anything in bulk: does this description pass the fact-check, yes or no? If yes, ship it. If you’re unsure, it goes to a human. Then measure what happened. A/B test the rewrite, watch the return rate, read the reviews. The edit isn’t done when you hit publish. It’s done when the data tells you it worked.
— Jamie Moss
Get your product listings edited and published faster
Merchup AI gives ecommerce teams the same specs-first, template-driven workflow covered above, but built to run across an entire catalogue rather than one listing at a time. Instead of manually checking specs, rewriting tone, and reformatting for Shopify one product page at a time, you generate, edit, and bulk-publish descriptions from a single visual editor that pulls product data straight from your store.
That matters most for teams juggling large catalogues where a manual, description-by-description edit simply doesn’t scale. MerchUp includes drag-and-drop templates, real-time catalogue management, and live Shopify integration, with support for additional platforms in development. If you want to see the drafting-to-editing flow in practice, the 30-second MerchUp tutorial walks through a live example. Plans run from Starter at £12.99 per month up to Scale at £149.99 per month, with Growth and Pro in between; full details sit on the MerchUp pricing page. Start with your highest-traffic SKUs and see how much editing time you save in the first week.
Sources
- AI editing (Jenni docs)
- Manage your Business Profile description - Google Business Profile Help
- Humanize AI text: Free AI Humanizer | Grammarly
FAQ
How do I edit an AI generated text?
Check every factual claim against a source document first, then rewrite for tone and benefits, then format for the platform where it will appear. Tools with a side-by-side diff feature let you accept or reject individual changes rather than rewriting the whole passage.
Where can I edit AI text for free?
Free options include Canva’s Magic Write for quick rewrites, Grammarly’s free tier for grammar and tone adjustments, and Quillbot for paraphrasing. None of these verify facts for you, so a manual accuracy check still comes first.
How can I edit an AI generated image?
That’s a separate task from text editing and generally needs an image editor (Canva, Photoshop, or a platform’s built-in tool) rather than a text-focused workflow; product description tools like MerchUp focus on written copy, not image editing.
How do I edit already written text?
Apply the same three-step logic: verify any factual claims, check the tone against your brand’s existing copy, then tighten for length and platform format. If you’re editing product copy specifically, running it through a humaniser tool can smooth stiff phrasing once the facts are confirmed.
How much does MerchUp cost?
MerchUp’s plans run from Starter at £12.99 per month through Growth, Pro, and Scale at £149.99 per month, each with different bulk-editing and template limits, listed in full on the pricing page.
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