LLM Optimisation: How Your Store Shows Up in AI Answers

“Which standing desk under 500 euros is any good?” – questions like this are increasingly answered by an AI: ChatGPT, Perplexity, Claude – and above all Google itself, whose AI Overviews reach around two billion people a month by its own account. The answer names three products and two stores. Either yours – or your competitor’s. Visibility in AI answers (often called GEO, Generative Engine Optimization) has moved from experiment to a discipline in its own right alongside SEO – with measurable signals, a mature tool landscape, and the first solid studies on what works and what doesn’t.

Key takeaways

• AI answers draw on three sources: training knowledge, live search results, and structured data that agents and shopping systems read directly.
• The biggest generative surface is Google itself: AI Overviews reach around two billion users a month, AI Mode around one billion – whoever ranks well and answers questions directly gets cited.
• Citable content – facts, tables, honest FAQs – gets cited; marketing fluff doesn’t.
• Honestly assessed, llms.txt is an unproven extra: costs nothing, does no harm – but Google declares it unnecessary and studies measure no effect.
• For stores, the ChatGPT product feed per the OpenAI Product Feed Spec is the most direct visibility channel – with updates on a 15-minute cycle.
• Measurement has grown up: tools like Profound, Peec and Otterly automatically track mentions, citations and share of voice in AI answers.

How LLMs form recommendations

Three sources feed AI answers to buying-advice questions: the model’s world knowledge learned during training, live search results in web-enabled systems such as Perplexity, ChatGPT Search or Google’s AI search, and increasingly structured data – schema markup and product feeds – that agents and shopping systems read directly. You can invest in all three, just on different time horizons.

Training data works slowly, over months and new model versions – what matters most here is whether your content is present in the open web and citable whenever a model gets trained. Search integration, by contrast, works almost instantly: improve your classic ranking today, and it can show up in an AI tool’s very next live search. Structured data, finally, is the lever gaining weight fastest as agent usage grows – more on that in our article on agent-ready commerce.

The biggest surface: Google AI Overviews and AI Mode

If you only think of ChatGPT when it comes to AI visibility, you’re overlooking by far the biggest generative surface: Google increasingly answers search queries itself. AI Overviews – the AI summaries above the classic results – reach around two billion users a month according to Google, and the conversational AI Mode passed the one-billion mark in 2026. For stores that means: whether you show up in AI answers is decided at Google first.

The good news: no secret rules apply to AI Overviews. Google preferentially cites pages that already rank well – classic ranking acts as an upstream filter – and that answer a question directly and extractably. If you deliver the most precise answer to conversational, multi-part queries (“electric standing desk under 500 euros, quiet, for a home office”), you hold the best cards. Notable in practice: according to one analysis, AI Overviews and AI Mode cite the same URLs in only around 14 per cent of cases – so both surfaces deserve their own monitoring.

An honest assessment includes the flip side: many AI answers end without a click. All the more important that the answer names and links your brand correctly – visibility is measurably shifting from clicks to mentions.

Creating citable content

LLMs love clear, fact-rich answers and avoid vague marketing language – comparison tables, specifications, honest pros and cons, and substantial FAQ blocks all get cited disproportionately often. The mechanism behind this is simple: a model composing an answer looks for extractable facts, not mood.

That has a direct consequence for product pages: thin, interchangeable content – identical descriptions across colour variants, for example – gets penalised by search engines and ignored even more thoroughly by language models, because it offers no extractable value. Our article on AI product copy at scale describes how to avoid this with copy generated at scale.

What works particularly reliably: structured comparison tables with clear column headings, numbered step-by-step instructions, FAQ blocks that open with a direct answer, and definitions that pin down a term precisely in one or two sentences before the deeper explanation follows. Plain prose paragraphs with no discernible structure, on the other hand, get cited less often, even when they’re just as factually correct – the model first has to laboriously distil the facts out of the text instead of lifting them directly.

From experience: a simple test shows whether a page is built to be citable: can you extract a correct, standalone answer to the heading from the first two or three sentences of a section? If not, the actual information sits buried too deep in the text for a model to reliably pick it up.

Establishing entity clarity

Brand, products and company need to be recognisable as clear, unambiguous entities so a model can correctly place them in context. In concrete terms, that means: consistent name spelling across every channel, clean Organization and Product schema as JSON-LD, and presence in sources models tend to trust – Wikipedia-adjacent sources, trade media, reputable review platforms.

In practice, that means writing your company name, brand name and product names identically on your own website, in marketplace listings, in press mentions and on review platforms – a company that shows up sometimes as “blicht GmbH”, sometimes as “Blicht” and sometimes as “blicht.com” makes it needlessly hard for models to map all three mentions to the same entity. A clean Organization schema with name, logo, contact details and social profiles on every page isn’t a nice-to-have here – it’s the machine-readable confirmation of that identity.

Machine readability and freshness signals

AI crawlers first need to be technically able to read what’s on the page at all – that sounds trivial, but in practice it fails at the same points as classic SEO often does. GPTBot, ClaudeBot & co need to be allowed in robots.txt, and content should be delivered as clean HTML rather than a JavaScript facade that, in the worst case, a crawler sees as empty.

Freshness signals have firmly joined the must-do list in 2026. For time-sensitive questions, models and their search backends visibly prefer well-maintained sources: a correct dateModified in your schema markup, lastmod values in the sitemap that match the real change dates, and visible last-updated dates on the page. According to one analysis of AI citations, around half come from content less than three months old. Especially relevant for stores: outdated prices, availability and statistics disqualify you – pages with numbers need a fixed refresh cadence.

llms.txt: an honest assessment

Hardly any GEO building block has been hyped like llms.txt – a curated markdown summary of your most important pages, meant as an entry point for AI systems. By now there is data, and it is sobering: Google’s 2026 guidance on AI search explicitly clarifies that llms.txt is not needed for AI Overviews or AI Mode and has neither a positive nor a negative effect. Otterly measured over 90 days that only around 0.1 per cent of all AI crawler requests fetch the file at all, and an analysis of roughly 300,000 domains found no measurable link between llms.txt and citation frequency in AI answers.

The honest line is therefore: costs nothing, does no harm, benefit unproven. We use llms.txt on blicht.com ourselves – as a maintained table of contents for machines, the half hour of effort is justifiable, and some AI vendors still recommend the convention for documentation sites. But it belongs at the end of the list, not the beginning: no substitute for structured data and citable content. If you only have one hour, put it into schema markup.

ChatGPT shopping: the product feed as a direct channel

Since 2025 there has been a route into AI answers for stores that has nothing to do with content: the product feed. OpenAI puts shopping queries in ChatGPT at around 50 million per day – and which products appear there is determined by the OpenAI Product Feed Spec. It defines, based on Schema.org, which product data OpenAI expects, and accepts updates on a 15-minute cycle: prices and availability can be accurate almost in real time. If you can deliver your data, you’re in; if you can’t, you simply don’t exist in this channel.

The pattern behind it is called “discover in AI, buy on site”: discovery happens in the chat, buying increasingly happens back in the store – the feed handles findability, your product page handles the close. Modern store systems increasingly generate these feeds automatically; your job is the data quality behind them: complete attributes, honest availability, consistent prices. How the UCP and ACP protocols fit together, and what Shopify’s growth numbers reveal about the channel, is covered in the agentic commerce guide.

Collecting mentions, not just links

LLMs weigh where and how often a brand shows up in the context of a topic – not just who links to whom. PR, reviews and community presence therefore pay directly into AI visibility, even with no classic backlink at all. Customer reviews play an underrated role here, because they give models additional, credible mentions that extend beyond your own website.

The most common mistakes in LLM optimisation

Four patterns prevent visibility in AI answers in practice:

1. AI crawlers blocked: out of privacy or control concerns, GPTBot gets blanket-blocked – and the brand disappears from the channel completely. Fix: a deliberate, documented decision instead of blocking by default.
2. Only marketing language, no facts: product pages full of adjectives but without specifications give a model nothing to cite. Fix: add concrete facts and comparison data.
3. Inconsistent brand presentation: different spellings or contradictory information across channels prevent entity recognition. Fix: maintain name spelling and core facts centrally.
4. No monitoring: nobody regularly checks whether and how the brand shows up in AI answers – gaps only surface once revenue is noticeably missing. Fix: establish a fixed review cadence, via manual audit or a GEO tool.

Measuring: manual audit plus GEO tools

Before you optimise, measure your baseline: our free LLM Brand Check asks the major models in one minute what they know about your brand or domain – the basis for everything that follows. Building on that, a simple, repeatable audit process makes GEO visibility tangible:

1. Compile a list of your category’s twenty most important buying questions.
2. Ask these questions in ChatGPT, Perplexity and Google’s AI Mode every month and log whether and how your store gets mentioned.
3. Note the sources cited in the answers – they often reveal which of your own pages, or which third-party sources, actually get read.
4. Track referral traffic from AI sources separately in your own analytics, rather than letting it disappear into “other sources”.
5. Close any noticeable gaps (questions with no mention) with targeted, citable content.

What was pure manual work two years ago is now a mature tool category: specialised GEO trackers automatically measure whether and how a brand appears in AI answers – as a mention, as a cited source, and as share of voice against competitors.

From experience: for most stores, the pragmatic entry point is a combination: the monthly manual audit (one to two hours) plus an affordable monitoring tool. Investing in the bigger platforms only pays off once AI referral traffic in your own analytics is demonstrably growing.

The bottom line

The channel has outgrown its experimental phase: Google counts its generative surfaces in billions of users, OpenAI its shopping queries in tens of millions per day, and with mature GEO tools measurability has arrived. If you’ve mastered classic SEO, you don’t need to learn a whole new discipline – you need to extend existing strengths with citability, entity clarity, structured data and freshness, and take the product feed seriously for the shopping channel. The most pragmatic first step remains the same: ask your category’s twenty most important buying questions yourself this week in ChatGPT, Perplexity and Google’s AI Mode, and honestly check whether your store shows up at all.