What is a knowledge cutoff?
A knowledge cutoff is the date on which an AI model's training data ends. The model has learned nothing about events, products or changes after that date, so without web search it answers as if time stopped there.
Every large language model has one, and most model providers list it in their documentation. Because training and testing take time, a model is usually released some months after its cutoff and may stay in use long after that. Knowledge just before the cutoff is often thin as well, because little had been written about recent events when the training data was collected.
Many assistants work around the cutoff by searching the web. With retrieval-augmented generation, they fetch current pages before answering. But not every question triggers a search, and some models and apps answer from memory by default.
Why the knowledge cutoff matters for your brand
Anything that changed after the cutoff is invisible to the model's memory: a rebrand, a new product line, new pricing, an acquisition or a new market. When an assistant answers from memory, it may describe an older version of your company, or not know you at all if you launched recently.
Over time, the cutoff also works in your favor. Each new model generation is trained on newer data, so the content and coverage you build now can shape how future models see your brand. That is one reason content freshness matters: current, clearly dated pages feed both today's search-based answers and tomorrow's training data.
How to deal with knowledge cutoffs
Test with and without search. Ask about your brand in assistants that search the web and in models that answer from memory, then compare what they say.
Announce changes clearly. After a rebrand, launch or price change, publish a dated page that states the change in plain words, for example "formerly known as".
Update the wider web. Refresh profiles, directories, review sites and partner pages so retrieval finds consistent, current facts everywhere.
Keep pages crawlable. Assistants can only correct outdated knowledge with search if AI crawlers can read your current pages.
Recheck after new model releases. A new model can change how your brand is described from one day to the next.
Trackbase runs your prompts daily across 11 AI models, so you notice when answers change. Read more about how to monitor changes in AI answers over time.
Examples of knowledge cutoff effects
A B2B payroll software vendor changed its name last year, but an assistant answering from memory still uses the old name and mentions plans that no longer exist.
A Dutch bicycle webshop recently added electric cargo bikes, yet a model with an earlier cutoff describes it as a shop for city bikes only.
A marketing agency founded after a model's cutoff is missing from its answers entirely, until the assistant searches the web and finds the agency's site.
An assistant quotes last year's subscription price for a meal-kit company because the price change happened after its cutoff.
Frequently asked questions
What is ChatGPT's knowledge cutoff?
It depends on the model version, and it changes with every new release. OpenAI lists the knowledge cutoff for each model in its documentation. Keep in mind that ChatGPT can search the web, so it may still answer questions about recent events. For your brand, test the prompts that matter instead of relying on a single cutoff date.
Can I get a new product into an AI model before the next training run?
Not into the model's memory, but you can make it findable for assistants that search the web. Publish a clear, crawlable page about the product, make sure it is indexed and earn coverage on sources such as reviews and trade media. Assistants that search can then retrieve and mention it, and future models may learn about it from the same sources.
Why does AI give different answers about my brand with and without web search?
Without search, the model relies on training data that stops at its knowledge cutoff, so it may repeat outdated or incomplete facts. With search, it reads current pages and bases its answer on them. A big gap between the two is a sign that older information about your brand still dominates what the model learned.