Training Data what a model learned before launch.

Training data is the large body of text a language model learns from before it is released, and it becomes the model’s built-in memory. Prefer focuses on what happens after training: it tracks the live answers ChatGPT, Gemini, Perplexity, Google AI Overviews and AI Mode give your buyers, and the sources they cite. Training data matters, but it is frozen at a cutoff date and you cannot edit it.

How training data works#

A large language model is trained by reading a huge amount of text, much of it public web pages, and learning patterns from it. What it learns is stored in the model’s weights. Researchers call this parametric memory. The 2020 RAG paper by Lewis et al. set it against non-parametric memory: documents a model looks up at answer time.

Training stops at a cutoff. Anything published after that date is not in the model’s memory until a new model is trained. This is why AI assistants now search the web for current questions, a process called grounding.

Training crawlers vs search crawlers#

AI companies run different crawlers for training and for live answers. The difference decides what blocking a bot actually does.

• OpenAI. GPTBot crawls pages that may be used to train future models; OAI-SearchBot is the crawler behind ChatGPT search results.
• Anthropic. ClaudeBot collects training data; Anthropic documents its crawlers and how site owners can block them in robots.txt.
• Google. Google-Extended is a robots.txt token that controls whether Gemini models can use your pages for training and for grounding in Gemini apps. It does not affect Google Search or AI Overviews.

Blocking a training crawler keeps your pages out of future model training. It does not remove you from live search answers, which rely on the search crawlers. Our answer on whether ChatGPT is blocked from your site walks through checking this.

Why it matters for AI search visibility#

Training data shapes what a model says when it does not search. That happens most on short, definitional prompts. If your brand is new or small, the model may know nothing about you, or worse, guess.

We saw this on our own brand. On our day-zero baseline, a model answered “What is Prefer?” from memory and got it wrong. In a later measured run, ChatGPT searched, retrieved our site and cited it correctly. Our grounding page has the full run: 47 of 50 answers were grounded in live sources, and only 3 came from memory.

What you can and cannot influence#

You cannot submit pages to a training set, and you cannot remove a fact a released model has already learned. What you can do falls into two groups.

For today’s live answers:

• Keep search crawlers allowed so engines can retrieve your pages when they ground an answer.
• Publish clear, current facts on your own site, written so a single passage answers the question.
• Get named on the third-party pages engines already cite for your category.

For future models:

• Decide on training crawlers. Allowing GPTBot, ClaudeBot or Google-Extended lets your public pages be considered for future training. Blocking them keeps your pages out. Either is a valid choice; it is a business decision, not a visibility fix.
• Be consistent across the web. If your name, category and core facts match everywhere, a future model has a better chance of learning them correctly.

How long until a model learns about you?#

There is no fixed answer, because it depends on when each company trains its next model and what data it uses. Our answer on how long ChatGPT takes to update what it says about a brand covers this in more depth. The short version: live search can reflect a change within days, while training data changes only with a new model.

Common confusions#

• Training data vs the search index. Training data is baked into the model. A search index is looked up fresh for each answer. You can influence the second this week; the first only changes with a new model.
• “Get into training data” as a goal. There is no submission form. See how to get your brand into ChatGPT’s training data for why live search is the better target.
• Training data vs fine-tuning. Fine-tuning is extra training a company does on top of a base model. Outside brands have no access to either.

Example#

A company renamed itself last year. Ask an assistant about the new name without web search, and it may not recognize it, because the rename came after the training cutoff. Ask with search on, and it can find the company’s site and cite it. The fix is not to chase the model’s memory. It is to keep current, consistent facts on your own site and on the pages engines retrieve.

How Prefer helps#

Prefer shows whether each engine’s answer about you cites live sources, and which ones. Its free Crawler Access Checker shows which training and search crawlers your robots.txt allows today.