Definition
A context window is the maximum amount of text, measured in tokens, that a language model can consider in a single pass. The instructions, the conversation so far, any retrieved documents and the model's own response all consume it. When the window fills, the earliest content is dropped or summarized, and once a session ends the window is discarded entirely.
Why it matters
The context window is routinely mistaken for memory. It is not memory; it is working attention, and it resets. A larger window lets a model hold more at once, but it does not let an organization accumulate anything: the same context is re-supplied and re-paid for on every request, and it disappears when the session closes. This is why enterprise AI can feel brilliant in one window and amnesiac in the next, and why the answer to organizational amnesia is a memory layer rather than a larger window.
Where it comes from
A technical property of transformer language models, not a Timer coinage. It appears in the glossary because the distinction between a context window and Organizational Memory is the single most common confusion in enterprise AI architecture.
Read the source papers: The Sovereign Memory Layer