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Content that AI models see
Language models - such as ChatGPT, Gemini, Copilot and Perplexity - are becoming the new layer of search. Increasingly, they are the ones prompting usersLanguage models (LLMs)-such as ChatGPT, Gemini, Copilot and Perplexity-are becoming the new layer of search. Increasingly, they are the ones suggesting to users where to buy a particular product, "which mountain running shoes to choose," or "which cream will be good for sensitive skin." If you're running e-commerce, your product content is no longer visible only to Google - it's starting to be "read" and interpreted by LLM and aI-search systems for e-commerce.
In this article you will learn:
- how AI models "see" your store and where they get their data from,
- what features of product descriptions, FAQs and circular content help them recommend you specifically
- how to practically rebuild content to make it friendly to both people and language models
- how Semly's platform helps systemically create content that LLMs see and cite
How do you create content that AI models can see?
How do LLMs get information about your store?
Language models do not automatically know that your store exists. They use several sources:
- Web crawl and AI-bots - similar to Googlebot, special bots (e.g., OAI-SearchBot) scan pages, retrieving HTML content, headings, lists, tables, as well as structured data schema.org
- Search engine indexes - many LLMs rely on existing Google/Bing indexes and search results, which they then process into a generative response
- Product feeds and APIs - in marketplace or dedicated shopping assistant environments, products are delivered directly through product files or APIs
- RAG (retrieval-augmented generation) systems - shopping assistants build a local index of your store's content (categories, product cards, FAQs, blog) and from it retrieve snippets for responses
On this basis, they build a "simple model" of your store: what you are associated with, what you specialize in, and how well you meet specific user needs.
What exactly does AI "read" on your site?
For LLM, the key issues are:
- Visible content in HTML - headings (H1-H3), paragraphs, bulleted lists, tables, text next to buttons
- Structured data (schema.org) -
Product,Offer,Review,FAQPage,Articleare "shortcuts" to the most important facts about the product, offerings, reviews and educational content - Meta data and navigation elements - page titles, descriptions, breadcrumbs, category names
- Reviews and Q&A - textual reviews, questions and answers from customers are an important signal of trust and the basis for building an argument in recommendations
This information is then transformed into vectors (embeddings) and stored in vector indexes, from which LLM selects the most matching fragments for a given query.
What content helps LLM recommend just your store?
1. Clear alignment with purchase intent
LLM needs to understand, for whom is the product and for which application. The more specifically you describe the use scenarios, the easier it will be for the model to match your offering with the user's question.
Instead of:
"Running shoes, lightweight and comfortable."
Bet on:
"Lightweight mountain running shoes designed for trail runners covering distances of 20-60 km on rocky trails."
For LLM, the second version sends a clear message: "this is a product for the trail runner for long distances over difficult terrain."
2. Completeness of product information
Language models are eager to recommend products about which they can say something specific and true. So they need a full set of data:
- parameters: dimensions, weight, composition, capacity, power, material
- variants: colors, sizes, versions
- context of use: for whom, under what conditions, what can be combined with
- logistics: delivery time, return policy, availability
If this data only appears in graphics, PDFs or is scattered, LLM is less likely to interpret it correctly.
3. Content structure friendly to "parsing" by AI
The clearer the structure, the easier it is for the model to extract the right passage from it and quote it in the response:
- h2/H3 headings with clear section names ("Who this product is for", "Key benefits", "Technical specifications")
- scoring lists with features and benefits
- short paragraphs, without walls of text
- tables with comparison of key parameters
4. Structured data as a strong factual signal
For traditional SEO, schema.org is the way to go for rich snippets. For LLM, it is even a language of facts:
Product+Offer- price, availability, brand, categoryReview/AggregateRating- ratings, number of reviewsFAQPage- a set of questions and answers that the LLM can quote directlyArticle/BlogPosting- information about educational content and its connection to products
The lack of structured data doesn't put you out of the game, but it does make it significantly more difficult for AI to understand your offering. Implementing them in SEO / AEO / GEO logic for e-commerce can significantly improve the reception of your content by models.
5. Signals of trust and authority
Models are increasingly taking into account the reliability of the source:
- full details of the company (address, tax ID, regulations, return policy)
- visible contact information and viable support channels
- consistency of information in descriptions, FAQs and reviews
- no exaggerated, unverified promises ("miracle cure for everything")
Stores that appear transparent and accountable are more likely to be cited in AI responses.
6. Timeliness and consistency of content
LLM and AI-search systems take into account the freshness of the content - outdated information on pricing, availability or ingredients can cause a model to bypass your products in favor of competitors with better-maintained data.
Regular updates to descriptions, FAQs and tutorials increase your visibility in the "AI layer." In doing so, it's worth thinking about them in the context of long-term sEO trends 2026 and the four layers of optimization.
How to write product descriptions under AI models (and humans at the same time)?
Proposed structure of LLM-friendly product charter
- H1: Product name + key application for example, "XYZ mountain running shoes - for long distance trail runners"
- Intro (2-3 sentences). Brief explanation: what it is, for whom, in what scenario it will work
- H2: Key benefits. List of 4-7 points in the form of benefits (from the perspective of the user, not just technical features)
- H2: Who this product is for. Clear segments: level of sophistication, type of user, conditions of use
- H2: How it works/how to use it. Brief description of use, step-by-step instructions (if needed)
- H2: Technical specifications/composition. Table or ordered list of parameters
- H2: FAQ about this product. 2-5 most common questions and answers (also in schema FAQPage)
- Additional sections - "Frequently purchased from", "See similar products", with internal links
If you want a ready-made template, you can reach for the approach described in the article on the anatomy of an ideal product sheet under LLM models.
Example: an excerpt from a well-written description under LLM:
Key benefits:
- Cushioning tailored for long trail runs (20-60 km) on rocky trails.
- Aggressive tread for grip on wet rocks and mud
- Reinforced upper to protect toes and midfoot from stone impacts
- Breathable mesh fabric that quickly wicks away moisture during long workouts
Comparison: description under people vs under LLM
| Element | Only under the people | Only under LLM | Optimally |
|---|---|---|---|
| Language | Emotional, storytelling | Dry, technical | Clear, specific, with benefits |
| Structure | Long paragraphs | Short letters without context | Paragraphs + letters + H2/H3 |
| Technical data | Partial | Full, without explanation | Full + explanation of benefits |
| Usage scenarios | General | No | Precisely described |
FAQs that understand language models
How to formulate questions?
Write questions in FAQs as users speak to AI assistants:
- instead of "Delivery" - "How long does delivery take in our store?"
- instead of "Returns" - "How do I return a product?"
- instead of "Size selection" - "How to choose a shoe size for mountain running?"
This makes it easier for LLM to match a user's question to your FAQ and quote your answer.
How to write answers?
- start with direct response ("Yes, we ship products overseas...")
- only then add details, exceptions and additional guidance
- stick to a length of about 40-80 words - this is a convenient size for AI to quote
- avoid pouring water, focus on facts
Create three levels of FAQs:
- Global store FAQ - delivery, payment, returns, security, contact
- FAQs for categories - e.g. "Choosing the size of trail running shoes", "How to choose a cream for your skin type"
- FAQs for specific products - 2-5 questions and answers on the product card
Each of these levels means FAQPage schema with the actual questions and answers the user sees on the site. This is one of the cornerstones of practical GEO / AEO, discussed more extensively in gEO's guide to e-commerce and AI.
Circular content: blog, guides, rankings.
Educational content has a dual role:
- help the user make a decision
- build an image of your store in the eyes of the LLM as a expert in the field
How to design tutorials under LLM
- In the title, combine problem + product category: "How to choose shoes for running in the mountains? A complete guide with model recommendations"
- Introduction: clearly state who the guide is for and what problem it solves
- In H2/H3: educational sections (what to look out for, common mistakes), sections with specific product recommendations (with links to product sheets), FAQ section at the end of the article
Well-designed guides and rankings are also the basis for measuring the impact of the "AI layer" on sales - this part is worth supporting with analytics with the approach described in the article how to measure sales with AI-search.
Rankings and listings:
- Create content like "Top 5 products to..." with clear selection criteria
- For each product, describe for whom it is best (segmentation by level, budget, specific problems)
- Add a comparison table with key parameters
These are the kinds of articles LLMs like to quote when answering questions like "what are the best products for ...?"
Checklist - is your product description LLM-friendly?
Use this short checklist when working on each product card:
- Do you make it clear in the first 2-3 sentences what the product is, for whom and for what use?
- Do the H2/H3 sections have descriptive titles ("For whom," "Key benefits," "Technical specifications") rather than generalities like "Description"?
- Are all the key parameters in the form of text or a table, not just on a graphic?
- Do you describe specific usage scenarios that are easily matched with user questions?
- Does the product have Product + Offer + Review structured data implemented (if there are reviews)?
- Is there a mini-FAQ with 2-5 questions and answers (from FAQPage schema) next to the product?
- Are there links to related guides or blog rankings that elaborate on the topic?
- Is the data (price, availability, composition) up-to-date and consistent throughout the store?
The more "yeses" you mark, the greater the chance that language models will start quoting just your content more often.
How do you measure whether LLMs are recommending your store?
You can:
Manual query testing - regularly check ChatGPT/Gemini/Perplexity for answers to common questions in your category ("what kind of mountain running shoes?", "where to buy...?") and record whether your store shows up. The approach from the article can be helpful here: Ask ChatGPT why it doesn't recommend your brand
- Monitoring of brand "mentions" - track whether the brand/store name appears in responses; take screenshots before and after content changes
- Analysis of direct and branded traffic - an increase in direct hits and branded inquiries could mean that users are moving to you from AI recommendations
- Post-purchase surveys - add the question "How did you find out about our store?" with the option "recommendation of AI assistant (e.g. ChatGPT, Gemini)."
Platforms such as Semly.ai are developing their own methods to monitor the visibility of brands in the responses of various models, which allows them to observe the impact of changes in content on real AI recommendations.
How does Semly help create content visible in AI?
Semly.ai is a platform built specifically with a new layer of visibility in mind - the GEO/AEO (Generative / Answer Engine Optimization) and AIO (AI Optimization). Its goal is to help online stores and brands:
- increase the presence of models such as ChatGPT, Gemini, Grok, Claude or Perplexity in the responses
- clean up and rebuild content, including product content, FAQs and guides so that they are better "read" by LLM
- systemically develop content without having to manually write everything from scratch
In practice, Semly.ai helps with, among other things:
- AI visibility audits- shows how often and in what context your store appears in model responses
- Generate and optimize product descriptions - according to LLM-friendly patterns: with clear structure, described use scenarios and consistent data
- Building FAQs and educational content - suggests what questions are missing from your FAQ from the perspective of users using AI and how to formulate them
- Adapting content to the four layers of SEO 2026 - combining classic SEO, SXO, AIO and GEO into one cohesive content system
This way you don't have to guess on your own how to construct content "under LLM" you can rely on proven patterns, monitoring and tools designed just for e-commerce, in the spirit of the modern AEO/AI Engine Optimization.
Summary
Language models have become the new "interface" to the Internet. For many users, they are the first place they look for product and store recommendations. To make your e-commerce visible in this layer, you need content that is:
- complete (full details of products, logistics, applications)
- well structured (headings, lists, tables, FAQs, structured data)
- geared to real-world problems and use scenarios, not just dry features
- coherent and up-to-date throughout the store
If you combine these principles with a systems approach to GEO/AIO and use tools such as Semly.ai, your chances of being the one to have your products recommended by LLM increase very noticeably - as we discuss more extensively in the context of the aI competitive advantages in e-commerce.
FAQ
Do I need to write separate descriptions "under AI" and "under humans"?
No. The goal is to create a single, well-structured description that is both responsive and easy for LLM to process.
Without structured data, will LLM see my store?
Yes, but with much less precision. Structured data helps models understand more quickly what you are selling, at what price and to whom.
Where to start optimizing content for LLM?
First, from the key product sheets and global FAQ: organize the structure, add missing data and implement structured data. Then move on to key categories and guides.
When will I see results in AI responses?
It depends on how often your site is crawled by AI bots, but the first changes are often seen within a few dozen weeks of a major content redesign.
Can I do it by hand, without special tools?
Yes, but with more products it quickly becomes inefficient. Platforms like Semly.ai help scale content creation and updates while maintaining consistency in structure and language.
Sources
- AI SEO 101 - knwn.app
- How AI crawlers and bots read your site differently from search bots - Superlines
- How AI Shopping Assistants Recommend Products - Trustnoww
- LLM-Optimized Content Structures: Tables, FAQs & Snippets - Averi
- How to Optimize and Format Product Content for LLMs - Suso Digital
- Structured Data & Schema Markup Best Practices for AI Search - Geneo
- Store visibility in AI 2025 - Semly.ai
- Structured Data in 2024 - HTTP Archive / Web Almanac
- AI SEO Checklist - Rozenberger
- Semly.ai - #1 AI Tool for GEO in E-commerce
- 4 Layers of SEO 2026 - Semly.ai
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