How to Write Product Descriptions That LLMs Can Understand

In a 12-week analysis of AI-generated citations across ChatGPT, Perplexity, and Google SGE, product-related content accounted for 52% to 68% of all cited results. The study covered over 710,000 citations across ecommerce, health, and consumer product queries.

The findings confirm that product detail pages (PDPs) are now a primary surface for AI visibility.

Unlike traditional search engines, large language models don’t rank based on keywords. They extract structured meaning identifying what the product is, who it's for, when it's used, and why it matters.

When descriptions are clear, structured, and attribute-rich, they’re cited in AI shopping panels, comparison modules, and generative answer summaries. When they’re vague, they’re ignored.

How LLMs Read Product Pages for Recommendations

Large language models treat product detail pages as structured input not creative copy. They parse descriptions line by line, extracting attributes that help match products to the intent and constraints of the query.

Consider the prompt:

“Best black one-piece swimsuits with tummy control under $100”

In ChatGPT, the result is a structured shopping panel containing:

• Product name and price

• Color: black

• Features: “tummy control,” “adjustable straps”

• Sentiment: 4.5-star rating with review count

LLMs analyze product pages line by line to extract matchable attributes. They extract:

• Product category and features

• Color and fit descriptors

• Price and availability

• Functional attributes (e.g. tummy control, adjustable straps)

• Customer sentiment and review count

• Brand recognition or trust indicators

The PDP is scanned by GPTBot (or PerplexityBot), cross-referenced with structured schema, and matched against query constraints. The more aligned and extractable your description, the higher your odds of inclusion.

If your description lacks these signals or buries them in ambiguous copy, your product gets skipped.

What matters most:

• Use clear, scannable attribute phrases

• Reinforce visual content with structured copy

• Sync visible features with Product and Offer schema

The AI is not guessing. It’s parsing. Your product description is the query match system.

What Makes a Product Description Extractable to AI

AI platforms do not interpret content the way humans do. They scan for structure, clarity, and consistency, treating each product description as a data source, not a narrative. Descriptions that perform in ChatGPT and Perplexity aren’t just well-written; they are machine-legible.

Based on observed parsing behavior across LLM outputs, extractable product descriptions share five consistent traits:

1. Structured Attribute Stacking

Attributes like “color: black,” “fit: high-waisted,” or “feature: tummy control” must appear in plain language. When buried in long, narrative copy, these elements are missed. But when surfaced clearly, especially in bullet lists they get parsed and indexed.

Works for LLMs

A sleek black one-piece swimsuit featuring tummy control, adjustable straps, and a plunging neckline.

Gets ignored

Dive into summer with a suit that hugs you just right. You’ll feel amazing in this flirty, flattering silhouette.

2. Consistent Product Vocabulary

LLMs train on common ecommerce language patterns. Phrases like “medium support,” “moisture-wicking,” or “high-rise waist” are recognized across retail catalogs and help the model classify the product type.

Avoid proprietary or brand-invented language. Instead, use terminology found in product taxonomies, filter systems, and major ecommerce listings. If your copy uses niche adjectives or metaphorical language, LLMs deprioritize it due to uncertainty.

3. Contextual Completeness

LLMs try to infer:

• Who it’s for (e.g., plus-size, petite, athletes, travelers)

• When it’s used (e.g., vacation, gym, postpartum)

• Why it’s relevant (e.g., offers lift, supports recovery, flatters curves)

Descriptions that add this context are more likely to appear in longform generative answers and shopping guides.

4. Extractable Features in Bullet Format

Bullet points remain the most extractable content format across LLMs. Structured lists provide clarity, remove ambiguity, and segment attributes for quick parsing.

Use format like:

• Adjustable back tie for flexible fit

• Medium bust support

• Made with recycled nylon

• Full rear coverage

• Machine washable

These get extracted directly into structured product responses. Pages without bullet points or with scattered attributes are less likely to be cited or summarized.

5. Schema Support

Even the best-written PDP needs schema backup. ChatGPT and Perplexity rely on structured data like Product, Offer, and Review to confirm what the description says. Without schema, AI skips to the next best-structured option.

6. Canonical Alignment Between Page Elements

AI systems check for consistency between meta title, product name, H1 tag, and schema fields. If these elements mismatch—even slightly—the model may misclassify or skip your page.

Ensure that:

• Product name matches schema and URL

• Description aligns with meta data

• Schema fields are up to date and reflect visible content

• Noindex or disallowed paths are not blocking AI bots like GPTBot or PerplexityBot

How Erlin Optimizes Product Descriptions for AI Platforms

Erlin was developed in response to a clear gap observed across ecommerce audits: most product descriptions are not structured for AI extraction.

Even when the visual content and copywriting were strong, PDPs often lacked:

• Extractable feature phrasing

• Use-case context

• Consistency across metadata and schema

• Alignment between brand voice and AI-parsable language

To address this, Erlin generates product descriptions optimized for AI visibility without sacrificing brand tone.

How It Works:

• Input: a live product URL or raw product data

• Erlin parses existing page content, schema, and tone

• It generates a revised description that includes:

  • Clear, structured attributes

  • Industry-standard terminology

  • Contextual signals (audience, usage, benefit)

  • Alignment with existing schema fields

  • Brand-consistent language

Each description is designed to be machine-legible and brand-authentic, ensuring products are not only eligible for inclusion in AI rankings—but also reflect the brand’s voice and value proposition.

Book a demo to see how Erlin rewrites your product descriptions for AI visibility without losing your brand voice.

Erlin Writes in Your Brand Voice And AI Notices

AI systems don’t just parse content for features; they associate tone with credibility, consistency, and key messaging.

Descriptions that vary in style, vocabulary, or formatting across pages are more likely to be deprioritized. LLMs use tone consistency as a signal when classifying branded content, especially in competitive queries.

Erlin analyzes your existing content product pages, reviews, and category copy to understand your brand’s linguistic patterns. It maps:

• Common vocabulary and phrase structure

• Customer sentiment and recurring review language

• Brand traits (e.g., minimalist, expert-led, energetic)

• Formatting style across PDPs and metadata

Book a demo to see how Erlin rewrites your product descriptions in a tone AI platforms trust. 

Checklist to Make Your Product Descriptions AI-Ready

This is the standard your content and merchandising teams should follow when optimizing PDPs for AI platforms like ChatGPT, Perplexity, and Google SGE.

• Use clear, attribute-based language
  Surface key details like material, fit, finish, size, use-case, or feature in direct terms. Avoid branded adjectives or vague phrasing.

• Structure features visibly
  List important attributes in bullet format or early in the copy. AI extracts faster from structured layouts than dense paragraphs.

• Add contextual signals
  Explain who the product is for, what problem it solves, or when it’s most useful. Context improves placement in AI-generated buying guides and answers.

• Maintain consistent terminology
  Use industry-standard words—like “14k gold,” “high-waisted,” “lightweight,” or “moisturizing”—that LLMs are trained to recognize and compare.

• Support with schema
  Ensure every PDP includes valid Product, Offer, and Review schema. AI uses this to verify and rank content with greater confidence.

• Validate after publishing
  Use Erlin’s AI Visibility Dashboard to monitor which products are being surfaced in AI answers—and which need optimization.

Making your PDPs AI-readable is not just about copy, it’s about structure, clarity, and consistent signals that LLMs can extract and cite.

AI Product Discovery Is Already Here

Shoppers are asking AI what to buy and LLMs respond with product recommendations built from your PDPs.

If your descriptions lack clarity, structure, or context, they’re excluded. AI platforms prioritize what they can parse, attributes, audience fit, and usage, not just keywords.

This shift is already reshaping product visibility. Structured, machine-readable content isn’t optional. It’s the new standard for getting found.