{
  "dataset": "glossary",
  "record": {
    "id": "context-window",
    "term": "Context Window",
    "category": "knowledge-memory",
    "short_def": "The maximum amount of text (measured in tokens) a language model can consider at once — its prompt plus everything it has generated so far.",
    "long_def": "The context window is the model's working memory: everything the model can 'see' for a single response — system prompt, retrieved documents, conversation history and tools — must fit inside it. Larger windows let an agent hold more context, but cost and latency rise with how much of the window is used.",
    "see_also": [
      "token-economics",
      "tokenization",
      "context-engineering"
    ],
    "etymology_origin": {
      "value": null,
      "verify_status": "verify-against-primary-at-build",
      "source_hint": "https://en.wikipedia.org/wiki/Large_language_model — 'context window' is standard LLM terminology; no single coiner"
    },
    "related_to": [
      "token-economics",
      "tokenization",
      "context-engineering",
      "prompt-caching"
    ],
    "contrast_with": "Unlike a database that stores unlimited data, the context window is a fixed per-request budget — anything beyond it must be retrieved, summarized or dropped.",
    "example": "A model with a 1M-token context window can read a large codebase in one request; a 200K window forces an agent to retrieve only the relevant files.",
    "source": "https://en.wikipedia.org/wiki/Large_language_model",
    "status": "active",
    "why_it_matters": "The context window sets the Cost of Retrieval an agent pays: the cheaper it is to fit your content in-window (markdown twins, concise structured data), the more of it an agent can afford to read.",
    "sameAs": [
      "https://en.wikipedia.org/wiki/Large_language_model"
    ],
    "bridge_entity": "context-engineering",
    "last_verified": "2026-07-06",
    "md_twin": "/glossary/context-window.md"
  }
}