How to Create a Product Card with AI

A product card sells or doesn't sell long before the buyer reads the description to the end: the title and the first lines decide whether a person stays on the page or leaves for a competitor. How to describe a product with AI quickly and without losing quality is a question every seller with more than a dozen items in their catalog faces.

Below is a step-by-step breakdown of the process: from why creating the text takes so much manual effort to a specific example with a ready-made prompt.

Why a product card is hard

A good page requires not one skill but several at once: copywriting, a basic understanding of SEO and knowledge of a specific marketplace's requirements.

The main difficulties are:

• time: a quality description takes several hours of work — text, SEO, infographics;
• skills: you need copywriting, basic SEO and an understanding of the platform's specifics;
• scale: a seller often has more than a hundred items, and doing everything by hand quickly is unrealistic;
• competition: weak text means low search positions and fewer sales.

These difficulties are especially noticeable for sellers who run a catalog alone or with a small team: there simply aren't enough working hours in a week to write a hundred cards by hand.

At the same time, platforms regularly update their ranking algorithms, which means what worked six months ago may give weaker results today. Keeping up and rewriting descriptions manually after every change is a resource a small team usually doesn't have.

Text is not the only ranking factor: platforms also take visual content, reviews and price into account. But without good text even great photos and an attractive price won't keep a buyer on the page.

How AI writes the description

The process of creating a page breaks down into several steps, and at each of them a neural network speeds up part of the work.

Here are the main steps:

• description: AI writes SEO-optimized text based on the product's specifications;
• title: a catchy title with keywords within the platform's character limit;
• specifications: a structured description of the benefits, point by point;
• infographics: text for slides and, if needed, an image generated on request with a trigger word, for example "draw";
• reviews: templates for answering typical buyer questions under the page.

Each step can be done separately or combined into one request if you already have a full list of the product's specifications and understand which audience the page is for.

For the first item it's better to go through the steps one by one and look at the result of each: this makes it easier to see where the wording of the request needs clarifying. After a few descriptions you usually end up with a working prompt template that you simply adjust for each specific item.

It's worth keeping the tone of descriptions consistent: if part of the catalog is written in a business style and part with emotional epithets, buyers notice the difference and see the store as less professional.

Example of creating a card with AI

It's easier to see the process on a specific example with real input data.

The input for the example is:

• product: a thermal mug, 500 ml, stainless steel, five colors in the line;
• prompt: "write a description for Amazon: 500 ml thermal mug, stainless steel, keeps heat for a long time, five colors, add SEO keywords";
• result: a ready-made text in a few seconds with keywords and a clear point-by-point structure.

The more detailed the input in the request — volume, material, product feature — the fewer edits you'll need after generation. A general prompt without numbers and specifications usually returns overly generic text that has to be supplemented manually.

If the product has a feature competitors don't have — a special coating, a longer service life, a special set of accessories — state it directly in the prompt. Otherwise the model will build the description around the general specifications of the category, and the page won't differ from dozens of similar ones.

If the prompt is just "write a description of a thermal mug", the result will be vague: AI won't know the volume, the material or the product's feature, and the text will have to be rewritten almost completely.

Limitations and tips

Text from a neural network is a draft that saves time but doesn't replace the final check before publishing.

A few practical tips:

• check the facts: the model can get the product's specifications wrong if they weren't stated explicitly in the request;
• add uniqueness: template text that resembles competitors' descriptions performs worse;
• test variants: comparing two versions of a description shows which wording sells better;
• don't copy the result without checking spelling and whether it matches the product's real specifications.

Before publishing, check the text against the specific platform's requirements: the character limit in the title, the ban on superlatives without proof, the rules for mentioning a brand — they differ between marketplaces.

chataibot.pro has tools for generating descriptions for Amazon, eBay, Etsy and other marketplaces. Enter the product's specifications and get a ready-made text with SEO keywords in a minute, and keep the final check before publishing for yourself.

This approach is especially convenient when scaling a catalog: instead of hiring a copywriter for every new batch, a seller prepares a basic draft on their own and spends time only on targeted edits before publishing.

Once the process for one item is clear, the next step is to build a prompt template for the whole category and apply it to new arrivals without setting it up from scratch again.