What Is Training Data in AI?

Training data is the large body of text an AI model learned from before it was released. For a health practice, it matters because a tool answers from two places, what it learned in training and what it looks up right now. Your practice needs to be present in both.

How training data works

A large language model is built in a training run. The vendor feeds it a huge amount of text, and the model learns patterns about language and facts from it. When training ends, the model is frozen. It then gets released, often months later, and people use that same version for a long time.

That creates a date after which the model knows nothing. It is called the knowledge cutoff. OpenAI's model page for GPT-4o mini lists a knowledge cutoff of October 1, 2023. Any practice that opened, moved, changed providers or launched a new service after that date does not exist in that model's memory. Later models have later cutoffs, and every model has one.

Live search is the second source. Google's documentation for grounding describes it as connecting a model to real-time web content so it can answer questions about recent events and cite verifiable sources beyond its knowledge cutoff. OpenAI's page for the same model lists web search as a supported tool. When a tool searches, it reads current pages and writes from them. The step is explained under grounding.

Retrieval-augmented generation is the name for the method, in which a system fetches passages first and then writes from them. So a tool can answer in two modes.

• From memory. It writes from training alone, with no lookup. Names it has seen often are favored, and a young practice may be missing.
• From a lookup. It searches, reads pages and writes from what it finds. Hours, prices and new providers can come through correctly here.

Which mode a tool uses depends on the product and the question. A search-enabled tool does not search for every question. When it does not, it falls back on memory, and the answer may sound sure while being out of date. That is the failure described under hallucination.

Where does your website fit? OpenAI says GPTBot is used to crawl content that may be used in training its generative AI foundation models, and that its search crawler, OAI-SearchBot, is for search. The two are independent settings. Each can be allowed or blocked on its own. See GPTBot for the details. Vendors do not say which pages end up in training or how much weight a page gets, so I would not plan around it.

Think of it as a textbook and a phone. The textbook was printed on a fixed date and is full of what was known then. The phone can look up today's hours. A good answer often needs both, and a practice that exists only on the phone is easier to lose.

The honest summary is that memory is slow. It changes with each model release. Facts about your practice get into later versions only if they were widely and consistently available as text before that version was trained. Nobody outside the vendor can say how long that takes.

Example: training data at a health practice

This example is illustrative, not a transcript. A med spa opened in a mid-size city in early 2024. A patient asks an AI tool without search for the best med spa near them. The tool uses memory only, and its training stopped before the spa opened. It names two older spas that had years of reviews, press and directory listings by then. The new spa is not mentioned, and nothing the spa does this month can change that answer from that model.

Now the same patient asks a search-enabled tool. The tool looks the spa up, reads its site and Google profile and names it with its services. The gap between the two answers shows what is coming only from live search.

Health Biz Scale's D100 finding shows how thin memory can be even for established practices. We checked 764 established US health practices across 228 cities between May 11 and Aug 20, 2026, with one check per practice and test runs excluded. We asked GPT-4o-mini, through OpenAI's API and answering from memory with no web search, 2,292 patient-style questions. Only 6 of the 764 practices (0.8%) were named. The ChatGPT app searches live and can answer differently, so this is a floor and not a forecast.

A search-enabled tool still has to pick which pages to read, so the lookup depends on your site being indexed and readable. Training memory is also uneven by region and specialty, and no one outside the vendors can measure it directly. The lesson is not that AI ignores practices. It is that memory is a poor place to depend on being found, and the live lookup is where a practice can act.

How to check yours in 60 seconds

1. Open an AI tool that lets you turn search off, or use a model through its API playground.
2. Ask, with search off, "What do you know about [Practice] in [City]?"
3. Ask the same question with search on.
4. Compare the two answers. Write down what appears only with search on, and any fact that is wrong in either.

Use a temporary chat so your own history does not shape the result. Keep patient details out of any AI tool while testing.

What to do about it

1. Treat live search as the part you can improve this month. Keep your pages crawlable and your facts plain, as covered under ChatGPT search.
2. Fix every wrong fact at its source page or listing, since search-enabled tools read those.
3. Keep your name, address, phone, providers and services identical everywhere, so future training text agrees with itself.
4. Build real, durable mentions over time, such as local press, association pages and provider profiles.
5. Decide on purpose whether to allow GPTBot, knowing that blocking it affects training use and OAI-SearchBot controls search.
6. Retest each quarter and after each major model release, and note changes in a simple sheet.
7. Share the memory-versus-search test with your front desk lead, so they can spot a patient repeating a wrong fact from an AI answer and pass it to you.
8. Keep a record of the date and tool for every test, since model versions change and an old result may not describe today's tool.