Definition
Training data optimization is the practice of making accurate, consistent information about a brand, product or topic part of what future AI models learn during training. It targets the model's parametric knowledge, what it can say without searching the web, rather than the pages it retrieves at answer time. It is the slow, long-term half of GEO and LLMO.
Large language models are pretrained mostly on public web text, filtered and deduplicated. A large share of it traces back to Common Crawl, the nonprofit archive collected by CCBot. A 2024 Mozilla Foundation study cited by Common Crawl found that at least 64% of 47 language models published between 2019 and 2023 were trained on filtered versions of its data, and its September 2026 crawl alone contains 2.17 billion pages from 33.2 million registered domains. AI companies also run their own training crawlers: GPTBot for OpenAI and ClaudeBot for Anthropic, while Google uses the Google-Extended token to let publishers control whether crawled content is used to train Gemini models and to ground answers in Gemini Apps. Google says Google-Extended does not affect inclusion or ranking in Google Search.
Training data reaches users with a delay. Each model has a knowledge cutoff; OpenAI lists April 30, 2026 for GPT-6 Astra. Information published after the cutoff is invisible to the model unless it searches, and a correction made today shows up in parametric answers only after the next model is trained and released.
You cannot edit a model's training data, but you can influence what is available to it:
- State core facts (what the company does, its category, product names, founding date, leadership) identically on your site, profiles, press materials and documentation.
- Earn coverage in independent, well-edited sources such as trade publications, reference sites and widely read forums, because facts repeated across many sources are more likely to be learned.
- When you rename or retire a product, keep the old and new names together on key pages so models can connect them.
- Correct inaccurate sources, which otherwise keep feeding future training sets.
Two common misunderstandings are worth correcting. llms.txt is not a training control: it is a proposed file that summarizes a site for LLMs, and Google says its Search systems ignore it. Crawler access is controlled by robots.txt, and many publishers add a TDM rights reservation for legal opt-outs. Second, blocking training crawlers is a business decision with a trade-off. It keeps your content out of future training sets, which protects licensing value, but it also removes one source of brand facts. Search crawlers such as OAI-SearchBot and Claude-SearchBot are separate from training crawlers, and Anthropic's crawler documentation describes the three bots it uses for each purpose.
To measure progress, ask models about your brand with web search turned off and record whether they know it, describe it correctly and place it in the right category. Repeat with each new model version and compare. Answers that are right with search but wrong without it point to a training-data gap. Our analysis of why llms.txt has no impact on AI search explains the file's actual role.
Examples of Training Data Optimization
- A medical device startup founded in 2024 is unknown to assistants answering without search. It publishes a press kit with consistent facts, is covered by two trade journals and adds a Wikidata entry. A year later, newly released models describe it correctly without browsing.
- A news publisher blocks GPTBot, ClaudeBot and Google-Extended for licensing reasons but keeps OAI-SearchBot, Claude-SearchBot and Googlebot allowed, so it stays citable in AI search while opting out of training.
- A SaaS company finds models still describe a product it renamed in 2025. It adds a 'formerly known as' line to its product page, docs and app store listings so the two names appear together in future crawls.
- An SEO auditing a client site finds a security plugin had added a blanket disallow for CCBot and several AI crawlers. The team decides bot by bot which to allow, instead of keeping the plugin's default.
