How to Write Product Descriptions for ChatGPT and Gemini

How ChatGPT and Gemini read product descriptions

LLMs don't scan text the way a human shopper does. They tokenize the description, split it into chunks, and map those chunks to known entities. A product name like "Ultralight Trail Running Shoe" becomes a set of attribute-labels: category, weight class, use case. If the same description mentions "Vibram outsole" and "4mm drop," the model can link those to durability and ground feel.

But if you bury those facts inside a long, adjective-heavy paragraph, the model often misses them. It needs direct, self-contained statements. Write a description that works like a spec sheet written in plain English.

What a machine-readable description looks like in 2026

Start with the core entity. Tell the model what the product is, who makes it, and which variant it refers to. Use the exact product title you want AI to repeat. Then supply factual anchors: materials, dimensions, compatibility, certifications. These are the strings that LLMs pull when they assemble an answer.

Context matters more than keyword count. A sentence like "The SoloStove Bonfire fits a 19-inch log and cools down in under 15 minutes" gives two hard data points. A sentence like "The fireside experience is unmatched, perfect for your backyard" offers none. Models prefer the first type seven times out of ten, based on internal tests at Bilarna across 20 LLMs.

Pairing descriptions with structured data

Even a well-phrased paragraph benefits from a JSON-LD block that restates the same info in a machine-native format. When an AI Overview pulls a snippet, it often checks whether the page markup confirms what the prose says. Inconsistent data between the text and the schema lowers trust scores. Consistent alignment raises them.

Bilarna’s weekly audit checks that alignment across 56 signals. It flags mismatches between your Shopify product tags and the description text, then recommends the exact field to fix.

Writing for entity recognition and context

LLMs build a graph of your product from the nouns you feed them. Describe the item’s relationship to other things: “Compatible with DeWalt 20V MAX batteries” or “Designed to fit a 14-inch MacBook Pro.” These bridges let the model understand when to surface your product in a comparative query.

Skip the storytelling. A founder’s origin tale rarely helps a shopper who typed “lightweight camping tent under $200” into ChatGPT. Hard attributes: weight (1.8 kg), packed size (40 x 15 cm), waterproof rating (3000 mm). Give the LLM the same clean data you’d put on a shelf label.

How to structure attribute lists for AI

Make specification lists scannable. Bullet points in HTML (<ul>) signal hierarchy to language models. A list like this performs well:

• Weight: 290 grams per shoe (US 9)
• Midsole drop: 5 mm
• Upper material: recycled ripstop with TPU overlays
• Closure: speed laces plus ankle strap

Avoid cramming all that into a single, dense paragraph. The tokenizer loses boundaries. Bulleted facts stay intact and get quoted more often in AI-generated answers.

Structuring descriptions that AI citations can pull

When Google AI Overviews or Perplexity extract a direct quote, they often grab a short, self-contained sentence from the top 200 words of your page. So place your clearest one-sentence definition right after the product title. For example: “The Atlas 40 is a 40-liter backpack with a removable daypack and a load-bearing hip belt rated to 18 kg.” That’s a definition, a spec, and a differentiator, all in one line.

After that, expand with context, use-case examples, and technical notes. The order matters. Don’t lead with a vague headline like “Rediscover the Outdoors.” Start with the fact that a machine can repeat without distortion.

Common mistakes that reduce AI visibility

One pattern we see in Bilarna’s content audits: descriptions that are heavy on brand voice but light on concrete attributes. Phrases like “unmatched quality” or “crafted with passion” don’t map to any entity. They waste token budget. In a recent analysis of 500 Shopify product pages, those with fewer than four measurable specs appeared in AI-generated answers 62% less often than pages with eight or more.

Another mistake: writing for a single LLM. A description that shows up in ChatGPT might be completely ignored by Gemini because Gemini weights structured data markers differently. If you only test in one model, you’ll miss gaps. Bilarna’s monitoring covers 20+ models, including Claude, Grok, and Perplexity, so you spot the outliers fast.

Missing alt text on product images also reduces visibility. Multimodal models scrape image descriptions when the alt attribute is filled with a plain, factual label, not a keyword pile.

Testing your descriptions across multiple LLMs

Manual checks are simple. Open a chat session, feed the model your product page URL (if it supports browsing) or paste the raw description, and ask a typical buyer question: “What’s the best ultralight tent for two people?” Watch which phrases the model repeats. If it skips your key spec, rewrite that spec until it sticks.

Repeat the same prompt in Gemini, Claude, and Perplexity. You’ll often get three different responses, each lifting a different text section. That variability tells you where your copy is weakest. Bilarna’s AI visibility tracker automates that check weekly, giving you a single LLM Visibility Score that measures how often your brand appears across models.

Making updates that actually move the needle

Don’t rewrite a description on instinct. Use a gap analysis to find the missing topics and questions your competitors cover but you don’t. Bilarna’s content gap feature does that for you. It scans rival pages, extracts the entities they mention, and generates a prioritized list of attributes, questions, and comparison points to add to your product page. Then it auto-publishes the optimized version to your Shopify store or Framer site with one click.

After you publish, the platform monitors how AI answers change over the next two weeks. If a description update lifts your visibility score by even three points, that’s a measurable win. No guesswork.

Run a free AI visibility audit on your current product pages and see exactly which signals you’re missing for ChatGPT, Gemini, and AI Overviews.