AI product descriptions: where they help and where they hurt

A problem you really cannot solve by hand

If your catalogue has 3,000 products, writing a unique description for each of them is a project for a quarter, not a weekend. The usual result: a hundred products get polished descriptions and the rest are copied from the manufacturer - identical to the ones in twenty other stores.

That is a real cost: duplicate content does not build search rankings, and a customer who lands on the same impersonal paragraph they have seen everywhere has no reason to buy from you in particular. This is where AI does real work - not because it writes better than a human, but because it writes at all, where otherwise nobody would.

Where AI is unbeatable

• The long tail of the catalogue. Products that sell a few units a year and still need a sensible description and meta tags.
• Variants of the same product. The same hoodie in eight colours - AI will write eight different descriptions instead of one copied eight times.
• Turning specs into sentences. Converting the manufacturer's dry table into sentences a customer understands ("5,000 mm waterproof rating" → "handles an autumn downpour, not just drizzle").
• Meta titles and descriptions. Dull, repetitive work where people make mistakes out of boredom.
• Translations for other markets. Entering the German market without AI means a budget for a translation agency.

Where AI falls short

There are places where a generated description is visibly worse - and customers feel it, even if they cannot say why.

• Bestsellers and flagship products. The handful of items that make up the biggest share of your revenue deserve text written by someone who knows the product and the customer. Here the description does not inform - it sells.
• Products with a story. If you sew them yourself, source them from a specific craftsman or there is a story behind the product - AI does not know that story and will make it up. Made-up authenticity is worse than none.
• Anything meant to build the brand. Your brand's tone of voice is the most valuable thing in your store. Do not hand it to a model by default.

Three things that ruin AI descriptions

1. Feeding AI garbage. A model writes based on what you give it. If the product data holds only a name and a price, you get a paragraph full of generalities ("high quality", "perfect for any occasion"). Give it materials, dimensions, use cases and the target group - and you get specifics.

2. Broken grammar. In Polish this is a real trap: nouns and adjectives change their endings by gender and case, and models still get them wrong. „Czarny bluza" (a masculine adjective on a feminine noun) or „dla kobiet szukające" (a wrong case ending) are not typos to a Polish customer - they are a signal that nobody read the text before publishing. Check the grammar before you release the whole catalogue.

3. Publishing without reading. Generating 400 descriptions takes minutes. Reviewing them takes a few hours. That second step is what separates a store that gained from AI from a store that gained 400 problems.

A sensible process

Not "AI or a human", but a split of roles:

• Bestsellers and flagship products - a human writes them. Slowly, well, with the brand in mind.
• The rest of the catalogue - AI generates them from solid product data.
• Always - a human reviews before publishing and fixes whatever grates.
• Meta tags and translations - AI, with a quick look at the results.

DoSwiftly has no built-in description generator - you paste text from any AI tool into the product description in the admin panel. The product's meta title and description fill in from its name and description when you leave them empty - but the description itself is still worth writing by the rules above, with AI or without. See how the product catalogue works.