A knowledge cutoff exists because of how a language model learns: it is trained on a large collection of text, its training data, and does not keep learning on its own after training. What it absorbed, its parametric knowledge, stays frozen at the point where that text ends. For example, if a company changes its prices in March, a model whose training data ends in January can at most know the old prices from training, even when someone asks months later.
Two dates: reliable knowledge and training data
OpenAI and Anthropic list the cutoff of their models in their developer documentation; Google, as of October 2026, lists it only for some older Gemini models. Anthropic distinguishes two dates: the reliable knowledge cutoff, through which a model’s knowledge is most extensive and reliable, and the training data cutoff, which marks how far the data used in training extends overall. For some models, the two dates are several months apart. A model may know something about the months in between, but less reliably.
Even a stated cutoff is not a sharp line. A study presented at the COLM conference in 2024 examined openly available language models and found that their effective cutoff often differs from the reported one and, for some models, falls considerably earlier. One reason: even recent snapshots of the web, from which training data is compiled, contain a notable amount of older text.
Why the cutoff matters for GEO
For businesses, the cutoff matters most when something changes: a new product, a new company name, new prices, a new location, or a new contact person. A model knows little or nothing from training about what happened after its cutoff. If it answers from that knowledge alone, the new offering is missing, or the answer contains outdated details that mislead people much like a hallucination.
Timing adds to this: a model can remain available for a long time after its release, in Anthropic’s case sometimes for more than two years. An answer based on trained knowledge can therefore reflect information that is considerably older than the question. Only a new model version with a later cutoff can know about a change from training, and only if its training data contains it.
How newer information still reaches AI answers
Many AI systems work around the cutoff by retrieving current content from the web when needed and basing their answer on it. This approach is called retrieval-augmented generation (RAG). Anthropic explicitly describes Claude’s web search as a way to answer questions with up-to-date information beyond the knowledge cutoff. Whether an AI system looks something up on the web for a given question, however, depends on the system and the question.
For changes to your own offering, this path is crucial. New products, prices, or a new name should therefore appear on your website promptly, as text on a crawlable page: one that crawlers—programs that fetch web pages automatically—can reach and read. An updated XML sitemap, a file that lists a website’s addresses, helps as well. How publication and update dates are signaled to people and machines is covered under content freshness. When an AI system looks things up on the web, however, it only finds the page if the page is in its web index, the collection of captured web pages it draws on.
Because AI systems also retrieve third-party pages and future models learn in part from publicly available web content, it pays to correct outdated details in directories, profiles, and on partner websites as well. Such details on other websites are what Off-Page GEO is about.