Training Data
Training data is the corpus of text, images and other content an AI model like ChatGPT, Claude or Gemini learns from before it ever answers a question. It comes from crawled web pages, books, forums, code and licensed datasets. What a model can say about your brand off the top of its "memory" is bounded by whether you showed up in that corpus, and how. If you never appear there, the model has nothing to draw on when someone asks it about your category.
Why training data matters for your visibility
When someone asks an AI assistant to name the best providers in your industry, part of what it draws on is knowledge baked in during training (the rest comes from live retrieval, which is a separate mechanism). If your brand is named often, accurately and in a credible context across the pages that got crawled, the model is more likely to recall and recommend you. If you are absent, you are simply not part of the answer space. Unlike a search ad, there is no way to buy your way into a model's training corpus after the fact. This is a slow-moving, upstream lever, not a switch you flip before a launch.
How training data works
A language model is not handed finished facts about your company; it learns statistical associations between words, entities and contexts across billions of examples. If enough independent, trustworthy pages consistently link your name to a topic, that association gets reinforced. Two things matter beyond the corpus itself. First, every model has a training cutoff, so anything published after that date is invisible to the base model unless it also does live web retrieval, which is exactly why tools like ChatGPT, Gemini and Perplexity increasingly blend a frozen knowledge base with real-time search. Second, showing up on the open web is a different game from ranking on Google: an Ahrefs analysis of roughly 75,000 brands found web-mention frequency correlates with AI citation rate at about 0.664, roughly three times stronger than backlink counts at about 0.218, and a separate Ahrefs study found only 6-8% of URLs ChatGPT cites overlap with Google's top 10 for the same query. Being well-optimized for search does not guarantee you exist in a model's training-derived knowledge.
Common mistakes
The most common mistake is assuming classic SEO automatically covers this. Ranking well on Google does not mean a model learned who you are, because the two systems select and weight sources differently. A second mistake is inconsistency: if your company name, services or locations are described differently across the web, a model learns a blurrier picture and cites you less confidently, or not at all. A third and increasingly common mistake is treating an llms.txt file as a fix. Google has stated plainly that no special markup, schema or AI-specific file is required for AI Overviews or AI Mode, and in 2025 Google's John Mueller confirmed no Google Search system reads or acts on llms.txt; an Ahrefs analysis of roughly 137,000 sites that published one found about 97% saw zero measurable referral traffic tied to it. The fourth mistake is relying only on your own site. Models learn from a broad spread of independent sources, so mentions on portals, in directories and in editorial coverage do more for how you're represented than your own copy does.
Relation to AI recommendations
Training data is the lever behind whether an AI assistant recommends you at all. You cannot edit OpenAI's, Anthropic's or Google's datasets directly, but you do influence what future training runs are likely to learn about you, since new corpora are built from crawls of the same open web your content sits on. Every clear, fact-consistent, independently-published piece of content about your brand is a candidate for the next training round. This is the core premise behind Generative Engine Optimization, the practice (first formalized academically by Aggarwal, Murahari, Narasimhan, Deshpande, Rajpurohit and Kalyan in their 2023 paper, presented at ACM SIGKDD 2024) of shaping how visible and citable a brand is to generative AI systems. In practice that means earning genuine third-party mentions and clean, unambiguous entity information, not chasing technical shortcuts that these systems don't actually rely on.
Example
Imagine a mid-sized heat-pump installer that has spent years on Google Ads and little else. A prospective customer asks ChatGPT, "which companies install heat pumps in Freiburg?" The model answers from what it learned during training and names three competitors who turn up repeatedly across trade portals, local directories and how-to guides. The installer itself doesn't come up, because there is barely any independent, consistent content about it anywhere on the open web. Only once it starts appearing in trade coverage, local listings and guest articles, with its name, services and location described the same way each time, does it stand a real chance of being learned and mentioned in a future model.
Common questions
Can I change an AI model's training data directly?
No. You have no access to the datasets behind ChatGPT, Claude or Gemini. What you can do is shape what a future training run is likely to pick up, by publishing clear, consistent, fact-rich content across independent sites, since these corpora are built from crawls of the open web.
Does adding an llms.txt file or schema markup get me into training data?
No. Google has said no special markup, schema or AI-specific file is required for its AI features, and Google's own John Mueller confirmed no Google Search system reads llms.txt at all; one analysis of about 137,000 sites that added one found roughly 97% saw no measurable referral traffic from it. What actually correlates with being cited is genuine third-party mentions across the web, not a file you add to your own server.