Are your product descriptions ready for the AI world?

Product descriptions on most e-shops are invisible to AI—and this problem is getting worse every month.

Until recently, it was enough for the description to be readable, SEO-friendly, and help the customer make a decision on the product page. This worked in a world where choosing a product meant going through links and comparing several e-shops.

But this world is changing.

How the way customers discover products is changing

LLM systems—AI search engines, conversational assistants, and recommendation models—now summarize website content, answer customer questions, and suggest specific products.

They are not looking for the "prettiest text." They are trying to understand the product and its context.

If the product description does not provide clear and unambiguous information, AI will not classify the product correctly, will not be able to compare it, or will not include it in its recommendations at all. And that is exactly where most e-shops lose visibility today.

What LLM systems really need from product descriptions

LLM does not evaluate style or marketing tone. It evaluates information and meaning.

From experience, we see that AI systems need to have clear answers to the following questions in their description: What exactly is this product? Who is it suitable for? In what situations does it make sense to recommend it? When is it not suitable? What are its limitations, specifics, or compromises?

Without these signals, AI cannot distinguish the product from similar alternatives, does not know when to recommend it, and often prefers to omit it altogether.

Example: What does an LLM-ready description look like in practice?

Let's look at a specific example – the KitchenAid Artisan stand mixer. On the left is the original description from the e-shop, and on the right is the optimized version:

Comparison of original and LLM-ready product description (KitchenAid Artisan 185)

Note the key differences: the optimized description clearly defines who the product is intended for ("you'll appreciate it if you bake and cook often"), structures the benefits according to use, and includes specific information about expansion options. The AI system will clearly understand from such a description when to recommend the product.

The most common mistakes in product descriptions

E-shops tend to have very similar problems: it is not clearly stated who the product is (or is not) intended for, restrictions are concealed because "that's not what you write," usage is described in general terms without specific scenarios, the structure varies from product to product, and key information is hidden in long paragraphs.

For humans, such a description is often "good enough." For AI, however, it is ambiguous and unreliable.

Why this problem will get worse

LLM systems are gradually becoming the gateway to products. They determine what users see, what they compare, and what they are recommended.

The important thing is that there will be no "big crash" and no single clear problem that can be easily identified. Instead, e-shops will gradually become less visible, less recommended, and less relevant. Slowly. Unobtrusively. But systematically.

Why manually rewriting descriptions is not enough

The common response is simple: "Let's just rewrite the descriptions." In practice, however, this runs up against the reality of e-commerce—hundreds or thousands of products, multiple markets and languages, frequent changes in assortment, and the need for consistency.

Manually, this approach is expensive, slow, and unsustainable in the long term. Moreover, without clear rules, each description is created slightly differently. And inconsistency is a bigger problem for AI than poor stylistics.

How we approach product descriptions at LOCO

At LOCO, we view product descriptions as part of the e-shop infrastructure—just as important as feeds, prices, or inventory data.

This means a uniform structure across the catalog, clearly defined information that each description must contain, scalable automation, and content that both humans and AI can understand.

It's not about "prettier copy." It's about content that AI can understand correctly, compare, and recommend.

Practical test: Is your e-shop LLM-ready?

If you want to check how your product descriptions are doing, try a simple test. Copy the prompt below and paste it into ChatGPT or another LLM along with your product description:

You are an AI system evaluating a product description for AI-driven search and recommendation systems.

Analyze the following product description and answer these questions:

1) Is it clear what the product is and what category it belongs to?
2) Is it explicitly stated who the product is suitable for?
3) Is it clear in which situations the product should be used?
4) Is it explicitly stated who the product is NOT suitable for or what its limitations are?
5) Can the product be reliably compared with alternatives based on this description?

Rate each area from 0 to 10 and briefly explain what information is missing or unclear.

Product description to evaluate:
[PASTE PRODUCT DESCRIPTION HERE]

If you receive vague answers or low scores, this is a clear sign that the AI does not have enough information to properly understand and recommend your product.

Would you like an AI audit of your product content?

At LOCO, we work with e-shop product content every day. We see where AI loses context, trust, and the ability to recommend products.

If you want an objective evaluation of product descriptions from an LLM perspective, to identify systemic errors across the catalog, or to get specific recommendations on what to improve, please write to me.

Just send us a link to your e-shop or a few products, and we will prepare an AI audit of your product content.

— Petr Sedláček, LOCO

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