Marketing
AI for product descriptions
Feed an AI tool structured product data, such as materials, sizes, uses and care, plus two or three descriptions you like. Ask for drafts in a fixed template and generate them in batches by category. Then have someone check every fact against the supplier sheet. AI handles the wording; your data and review keep the catalog accurate.
Effort:
Medium
Time saved:
Often about half the writing time per product (estimate)
The problem today
E-shops with hundreds of products often copy supplier text or leave descriptions thin. That hurts search visibility, conversions and returns, but writing each one by hand takes weeks.
How it works, in plain words
It works like a careful copywriter who only writes from the product sheet you hand over and follows the same template every time.
What goes in: a spreadsheet of product attributes (name, material, sizes, use, care), your template and two or three descriptions you like.
What the AI does: it turns each row of data into text in your template, batch by batch, matching the tone of your examples.
What a person checks: an editor compares every claim, size and material with the supplier sheet and fixes anything the AI added on its own.
What comes out: finished descriptions loaded into your e-shop, Shopify or marketplace listings, often through a Make or Zapier flow.
It cannot know facts that are missing from your data, and when data is thin it tends to fill the gaps with vague or invented details.
How it could work for businesses like yours
Illustrative examples, not client case studies.
A 10-person online cosmetics shop in Thessaloniki
They add around 80 new products each season and copy the supplier's text, which also appears on competitors' sites. They would export ingredients, skin type and usage into one sheet and generate original descriptions per category in a fixed template. They would measure editing time per product and whether those pages start to get more visits from search.
A home-goods retailer with two stores and an e-shop in Heraklion
Half the online catalog has one line of text or none. They would start with the 50 best-selling items, fill in missing materials and dimensions first, then draft in batches. A sensible target could be a complete catalog within a quarter, with fewer customer questions about sizes.
A 25-person metal furniture maker in Volos
Their dealers ask for product texts in Greek and English, and only the engineer knows the specifications. He would fill in a spec sheet once, and AI would draft dealer-ready descriptions that he reviews in short sessions. They would track how many hours a month the engineer spends on product texts.
How to do it, step by step
Clean the inputs. Export product attributes from your e-shop or ERP into a spreadsheet: name, material, dimensions, use, care and what makes it different. Missing data is the main cause of weak descriptions.
Fix one template. Decide the structure once, for example a one-line hook, three benefits, specifications and care, plus a word limit.
Pilot on 10 products from one category. Compare drafts with your best existing descriptions and refine the prompt until editors change little.
Run batches by category. Use a spreadsheet workflow or your platform's built-in AI, so each row produces one draft in the same format.
Fact-check and approve. A person checks every number, material and claim against the supplier sheet before anything goes live.
Measure. Track time per description and, over a few months, conversion and return reasons on rewritten pages.
Tools to consider
Shopify Magic: generates descriptions inside the Shopify product editor from a few keywords. Caveat: it only works for Shopify stores, and output stays generic unless you give it real product details.
ChatGPT or Claude: flexible for templates, tone and Greek and English versions in one pass. Caveat: copy-pasting product by product doesn't scale; large catalogs need a spreadsheet or automation step.
Make or Zapier: connect a spreadsheet or e-shop to an AI model so each new product gets a draft automatically. Caveat: setup needs testing, and one bad prompt repeats its errors across hundreds of rows.
Data protection and GDPR
Product data is usually not personal data, so GDPR risk is low here. The bigger risks are accuracy and consumer law. A wrong material, size or safety claim can mislead buyers and cause returns or complaints. Never let the AI invent certifications, origin claims or health benefits. Make sure you are allowed to reuse the supplier text you feed in. If you add customer reviews to enrich descriptions, remove names first. Train the people who run the process, since AI literacy for staff has been an AI Act duty since 2 February 2025. For unclear claims, check with your legal advisor.
Mistakes to avoid
Giving the AI only product names, then publishing descriptions full of invented features.
Rewording supplier text so lightly that your pages still match every other shop selling the same product.
Skipping a fixed template, so each page in the catalog ends up with a different style and length.