Are your product descriptions ready for the AI world?

The product descriptions of most e-shops are invisible to AI, and this problem is getting worse every month.

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

But that world is changing.

How customers are discovering products now

LLM systems, AI search engines, conversational assistants and recommendation models, are summarizing website content, answering customer questions and suggesting specific products.

They aren't looking for the „prettiest text“. They're trying to understand the product and its context.

If the product description doesn't give them clear, unambiguous information, AI won't categorize the product correctly, can't compare it, or won't include it in recommendations at all. And that's exactly where most e-shops are losing visibility today.

What LLM systems actually need from a product description

LLMs don't judge style or marketing tone. They judge information and meaning.

From practice we see that AI systems need clear answers in the 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 trade-offs?

Without these signals AI can't tell the product apart from similar alternatives, doesn't know when to recommend it, and often skips it entirely.

Example: What an LLM-ready description looks like in practice

Look at a concrete example, the KitchenAid Artisan kitchen mixer. On the left the original e-shop description, on the right the optimized version:

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

Notice the key differences: the optimized description clearly defines who the product is for („you'll appreciate it if you bake and cook often“), structures benefits by use case, and contains specific information about expansion options. From this kind of description an AI system clearly understands when to recommend the product.

The most common mistakes in product descriptions

E-shops repeat very similar problems: it's not clear who the product is (or isn't) for, limitations are hidden because „you don't write that“, use cases are described generically without specific scenarios, structure varies product by product, and key information is buried in long paragraphs.

For a human reader such a description is often „good enough“. For AI it's ambiguous and unreliable.

Why this problem will get worse

LLM systems are gradually becoming the entry gate to products. They decide what the user sees, compares with and gets recommended.

Importantly, no „big crash“ is coming and there won't be one clear problem easy to identify. Instead, e-shops will gradually become less visible, less recommended, less relevant. Slowly. Quietly. But systematically.

Why manual rewriting isn't enough

The common reaction is simple: „Let's rewrite the descriptions.“ In practice it hits the reality of e-commerce: hundreds or thousands of products, multiple markets and languages, frequent assortment changes, the need for consistency.

Manual rewriting is expensive, slow and unsustainable in the long run. Without clear rules every description ends up slightly different. And inconsistency is a bigger problem for AI than poorer style.

How we approach product descriptions at LOCO

At LOCO we look at product descriptions as e-shop infrastructure, as important as feeds, prices or stock data.

That means a unified structure across the catalog, clearly defined information that every description must contain, scalable automation, and content that both humans and AI understand.

It's not about „prettier copy“. It's about content that AI correctly understands, can compare and recommend.

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

If you want to verify how your product descriptions are doing, try a simple test. Copy the prompt below and paste it into ChatGPT or any other 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 get vague answers or low scores, it's a clear signal that AI doesn't have enough information to correctly understand and recommend your product.

Want 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, identification of systemic errors across the catalog, or specific recommendations for what to improve, write to me.

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

— Petr Sedláček, LOCO