AI product descriptions: how to keep them unique and avoid copying the manufacturer

AI product descriptions are only unique when the model gets the facts from the manufacturer’s product sheet as raw material, and the store’s editors decide the content, structure and tone. You can’t skip this. If AI just paraphrases ready-made text, you get the same description as hundreds of other stores, with the sentences in a different order. Below I walk through the whole process: gathering data, writing the prompt, editing, and checking before publishing.

Why does copying the manufacturer’s description hurt your store?

Because your product page no longer stands out in any way. And if it doesn’t stand out, why would a search engine show that page and not another one? The same text ends up on dozens of domains, and duplicate manufacturer descriptions turn them into a set of identical pages. Google’s guidance on people-first content asks directly whether content based on other sources avoids simply copying or rewriting them and adds something of its own. Then there is the customer. They lose too, because they can’t find answers to the questions they ask before buying, and they can’t see how your offer differs from the competition.

Is generating product descriptions with AI enough to make the text unique?

No. A model’s paraphrase of the manufacturer’s text is still the same content, just told in different words. Uniqueness comes from new information: what the product is for, who it is for, how it compares with other items in your range, and how to answer the real questions buyers ask. But watch out, mass product description generation has its own trap. Hundreds of texts following one template, with the same phrases, look like assembly-line production (and, to be fair, that’s what they are). The same Google guidance lists this kind of production without proper care as a sign of low-quality content.

How to prepare data before asking AI for a product description

A good description doesn’t start with the prompt „write a description”. It starts with a complete set of data. Before you run the model, gather:

1. Facts from the manufacturer’s product sheet: specifications, materials, dimensions, compatibility with other devices or systems.
2. Questions from customer service, emails and comments under the product.
3. What sets your store apart, such as delivery, service and available bundles.
4. A description of the target group: who buys it and what they use it for.
5. Words your brand uses and words it avoids.

You verify the manufacturer’s data. The model doesn’t add specifications on its own, full stop. And treat the original description as a source of facts, not as text to rewrite.

Prompt and brand tone: how to write AI product descriptions in your store’s voice

A good prompt sets the role, the audience, the input data, the structure of the text and what is off-limits. I recommend splitting the structure into parts: benefit, use, specifications, answers to questions. Brand tone? Describe it once, in a few sentences, and attach that instruction to every product. That way the whole catalogue sounds consistent. At the same time, vary the structure between categories. A drill description needs a different order of emphasis than a face cream description, and one template for the whole store very quickly produces cookie-cutter texts.

Editing and checking before publishing

No description goes live without a human reading it. AI speeds up writing, sure, but it isn’t responsible for the facts. For me, editing AI-generated text means a fixed checklist:

• specifications match the manufacturer’s product sheet,
• no paragraphs repeated from the original description,
• correct language,
• keywords used naturally, without stuffing,
• answers to the questions buyers ask.

If your store runs on WordPress, the AI Content Editor plugin may come in handy. In its panel you choose the post type and the specific content to edit, and the tool fixes errors, optimizes the text for SEO and suggests better wording. Don’t like a change? You restore the previous version, and that’s it.

AI in an online store: where to start with a large catalogue

With the products that get the most traffic and sales. The rest of the catalogue can wait. When rolling out AI in an online store, first build a model for one category, see how the descriptions perform on the site, and only then scale up. The same rule applies to unique product descriptions in WooCommerce and PrestaShop, the platform doesn’t matter here. Oh, and there’s the law. The manufacturer’s text may be protected by copyright, so it’s safer to rely on the facts than to take over its wording.

AI product descriptions work when they combine the manufacturer’s facts, the store’s knowledge and human editing. One without the others won’t do the job. To start, take your ten most important product pages, rework them using this method, and only then apply this way of working to the rest of your range.

Frequently asked questions