Up to a few hundred products, the “manual” method can still work — someone writes the webshop's product descriptions one by one. With several thousand SKUs, though, that's mission impossible. By the time we'd reach the end of the catalogue, new products have arrived, certain specifications have changed, perhaps new language versions or sales channels are needed too, and despair sets in. Fortunately, there's no reason to panic.
Generative artificial intelligence offers an obvious solution to this problem. It can now produce a product description in seconds — and for exactly that reason, it's easy to use badly. Speed alone is worth little if the system invents incorrect technical specs along the way, repeats the same empty phrases across hundreds of pages, or makes claims the merchant can't back up.
Truly well-functioning AI-based content production therefore doesn't start with typing a clever prompt into a chatbot. First you need to get the product data in order, define the content rules, and build the review process that every generated text passes through.
Why have product descriptions become more important, not less?
At first glance, you might think that in a webshop built on images, videos and recommendation engines, text plays an ever smaller role. The purchase process, however, tells a different story.
According to the Baymard Institute's research — based on thousands of usability tests and analyses of leading webshops — 51 percent of the webshops examined deliver a mediocre or worse product page user experience. Their research also shows that missing or hard-to-interpret product information can be a genuine barrier to purchase. On the product page, the shopper is usually no longer browsing a category in general terms. They're trying to understand a specific product: deciding whether that model suits them, whether it's compatible with something, whether the size is right, what it can be used for, what's in the box, or how it differs from the other product open in the next tab.
Meanwhile, product discovery itself is changing. According to Salesforce's 2025 Connected Shopper research, 39 percent of shoppers already use artificial intelligence to discover products. Within Gen Z, that share is above 50 percent. This matters because a webshop's product data no longer has to be clearly processable only by shoppers and the traditional Google search engine. The same information can feed the webshop's internal search, a marketplace, a product feed, a recommendation engine, or an AI-based search system.
The importance of product descriptions is therefore beyond question today. There's no debate — we have to do them well if we want to succeed online.
It's not the prompt that makes an AI product description good
One of the most common mistakes is attributing too much importance to the prompt.
“Write a professional, persuasive, SEO-optimized product description.” On its own, this tells the model almost nothing. It doesn't reveal who the product is for, what information it can work from, which claims to avoid, what the brand's tone should be, or what to do when some data is missing.
For a webshop, how the AI handles information gaps is especially critical. In creative writing, it can sometimes help if the model phrases things more freely — with product data, the same freedom carries serious risks. Suppose all we provide about a cordless drill is that it's an 18-volt, compact device. A language model can easily add a torque figure, an RPM value or a battery capacity that sounds plausible but has nothing to do with that particular model.
That's why the first step is always the same: have source data of adequate quality. The product's name, brand, manufacturer part number, EAN or GTIN code, dimensions, weight, material, performance, compatibility, box contents, variants and every other relevant attribute should be available in structured form wherever possible. That's what the AI should work from.
Technical specs are not yet a product description
First, let's stress that one of AI's best uses is precisely this: translating dry product data into the customer's language. A spec sheet, for instance, says: “750 ml capacity.”
Let's be honest — the customer cares more about what that means in practice. For example, that the bottle holds a larger serving of drink, so on a hike or a longer trip it needs refilling less often. The same works for electronics, tools, clothing or even furniture. The model can connect an attribute with its practical meaning. But there's an important boundary.
From a 750-millilitre capacity it follows that the bottle holds a fair amount of liquid. It does not follow that the drink stays cold for 12 hours. Claims like that require separate data. That's why one of the most important instructions for a webshop AI system isn't “be creative” — it's precisely that in certain situations it must not be. If a claim can't be derived from the product data provided, it should be left out.
The AI needs to know who it's writing for
The same product can be presented in several ways. In the description of a multimeter aimed at home users, the first thing worth explaining is probably what basic measurement tasks it handles. In a trade webshop serving electricians, the measurement range, accuracy class or CAT rating can come to the fore much sooner. The AI can't know on its own which approach is right. That's why every well-built product description workflow must include the target audience. No need for a multi-page buyer persona — a few sentences are often enough.
Break your brand voice down into rules too
Instructions like “be professional” or “write naturally” are far too broad. You'll get much more usable results by spelling out what a natural tone means for you. A webshop might stipulate, for example, that product descriptions be factual and easy to understand, use no exclamation marks, avoid hyped-up marketing adjectives, and never call anything “revolutionary”, “unbeatable” or “perfect”.
Prohibitions improve the text surprisingly much — especially when thousands of products are involved. A human quickly notices when the same “perfect choice for everyone who” phrase appears on three adjacent pages. An automated system, on the other hand, will happily churn out ten thousand variations of it.
Don't generate just one piece of text
Once a verified product database is in place, it's a waste to produce only one long description from it. From the same data source you can generate the product name, the short summary, the detailed description, the technical specification, the meta title and meta description, in some cases a product FAQ, and the feed content needed for your various sales channels. But here an important distinction must be made...
A description that works on your webshop's product page won't necessarily be good as a Google Merchant Center feed. A marketplace may need different fields and a different format again.
What does Google think about this?
For a long time, a simplistic idea persisted about AI content: that Google penalizes machine-written text. According to Google's current documentation, the method of production isn't the deciding factor in itself. The search engine wants to surface content that is helpful, reliable and made primarily for people.
The real problem is mass-produced, low-value content — especially when its primary purpose is manipulating rankings. Google handles this under “scaled content abuse”.
That's a very important distinction. There's nothing wrong with a webshop using artificial intelligence to produce 5,000 product descriptions. What can be problematic is 5,000 nearly identical pages that contain barely any useful information, where essentially only a few keywords and product names are swapped around. So good SEO starts here, too, with product data quality and the customer's information needs.
“SEO-optimized copy” doesn't mean keyword repetition
AI's weakness is especially visible when it's simply instructed to produce an SEO-optimized product description.
You can easily end up with something like this:
“This cordless drill is an ideal choice for everyone looking for a reliable cordless drill. The cordless drill offers a practical solution…”
Linguistically flawless — and completely useless on a good product page. You'll get far better results if the model is given the product's real search context. What is it used for? What problem does it solve? What other products is it typically compared with? Which attributes do customers base their decisions on? From this information, the relevant phrases emerge naturally.
Google's guidance on AI-powered search features doesn't recommend any special “AI SEO” technique either. The principle remains the same: helpful, unique, well-structured content.