Eli here from TinyIMG. LLMs still draw from traditional SEO signals (titles, schema, backlinks) but lean more on structured data, reviews, and knowledge graphs. Good product feeds + clean JSON-LD help. Some stores are already being mentioned in AI answers.
What we saw that works really well for our TinyIMG customers - It’s really well prepared JSON LD, clear descriptions (optimized and easiliy readable), Shopify taxonomy + LLMs.txt.
Hi @Lyros, LLM like ChatGPT, Gemini, or Claude don’t crawl the web in real-time the same way Google does. Instead, they’re trained on a mix of public data, licensed content, and sometimes third-party integrations.
That means:
Traditional SEO signals (titles, schema, backlinks, etc.) still matter for Google and other search engines, and indirectly for LLMs too, because many AI models pull from search engine results or knowledge panels when answering.
Structured data, product feeds, and reviews are becoming more influential. These are easier for AI to understand and may feed into the knowledge graphs or APIs that models tap into.
I’ve seen early experiments where Shopify stores get surfaced in AI-driven shopping recommendations (mainly when products are well-optimized on Google Shopping, marketplaces, or review platforms).
“LLM SEO” is not a formal discipline yet, but positioning your store for AI visibility could become a new growth channel, similar to how “voice search optimization” emerged a few years back.
For now, the best approach seems to be:
Keep your traditional SEO fundamentals strong.
Use structured data/schema for products.
Optimize product feeds for Google Shopping and marketplaces.
Encourage and manage customer reviews, these carry weight across multiple platforms.
I think we’ll see more concrete tactics as LLMs integrate deeper with commerce platforms.
Optimizing your Shopify store for LLMs like ChatGPT is about making your information clear, structured, and authoritative. Focus on creating high-quality, detailed product descriptions and blog content that directly answer the types of conversational questions customers might ask an AI.
Technically, the most important step is to implement comprehensive structured data, also known as schema markup, for your products, reviews, and FAQs. This provides a clear, machine-readable summary of your content that AI can easily understand and trust.
Finally, build your store’s authority by encouraging genuine customer reviews and creating a logical site structure with internal links between related content. A store that is well-structured and rich with helpful, user-focused information is best positioned to be recommended as a reliable answer by an LLM.
Great question, Lyros. I work across AI and ecommerce and here’s what I’m noticing:
Traditional SEO still helps
Clean site structure, fast loading, clear meta titles, and good backlinks still matter. LLMs read the public web, so these basics make it easier for them to understand your store.
Structured data is key
Add product schema/JSON-LD, keep your sitemap current, and use product feeds like Google Merchant. Claim your Google Business or brand knowledge panel so your info is accurate in knowledge graphs.
Build trusted signals
Collect reviews, get mentioned on reputable sites, and publish helpful guides or FAQs. Models like ChatGPT often pull from sources they see as credible.
Check for AI mentions
Search your brand on tools like Perplexity or ChatGPT with browsing to see if you’re already being cited.
Think “AI Optimization”
Combine strong SEO, structured data, and brand authority.
Example: A Shopify coffee brand posts a detailed guide on choosing sustainable coffee, marks it up with schema, and shares it with bloggers. Over time, AI tools start referencing that guide when people ask for sustainable coffee recommendations.
In short: good SEO + structured data + trustworthy content is the best way to show up when AI tools recommend products.
Great question @Lyros, The biggest difference is that LLMs don’t work like search engine crawlers, which index data in real time. Even when LLMs have live search features, they’re not as extensive as search engines. Most LLMs rely on training data that can be outdated. You can even ask them, for example, in ChatGPT: 'What’s the latest you know?
The biggest good news is that many big tech companies are working on protocols that will allow LLMs to browse products and make purchases, as the prediction is that most consumers in the future will be LLMs. So there is a strong chance that your products will automatically become visible to the LLMs.
Great point, @shivam_kumar — you’re absolutely right that LLMs (Large Language Models) don’t operate like traditional search engines since they don’t index live data in real time. However, as Shopify merchants, it’s already worth preparing your stores for the LLM era of discovery.
Here are a few practical recommendations based on our experience as an E-Commerce & SEO agency:
Use structured data: Make sure your product, pricing, and availability information is properly marked up with schema.org. This not only helps Google but also ensures future LLMs can understand your content contextually.
Write semantically rich content: Product descriptions, FAQs, and metadata should be clear, contextual, and natural — LLMs “understand” meaning and relationships better than traditional keyword crawlers.
Optimize for performance & accessibility: Fast load times and accessible design (WCAG/BfSG compliance) improve both user experience and machine readability, which will matter even more in AI-driven discovery.
Stay ahead of emerging LLM protocols: Keep an eye on upcoming APIs and integrations that may allow LLMs to access product data directly (e.g., through OpenAI, Google Merchant Center, or future Shopify AI tools).
At Muthwerk, we’re already preparing our clients for a future where products need to be readable and understandable not just by humans — but by intelligent systems as well.
Traditional SEO signals (titles, schema, backlinks, etc.) still matter for Google and other search engines, and indirectly for LLMs too, because many AI models pull from search engine results or knowledge panels when answering.
Structured data, product feeds, and reviews are becoming more influential. These are easier for AI to understand and may feed into the knowledge graphs or APIs that models tap into.