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What is a Context Window?

A context window is essentially the span or range of tokens—units of text like words, subwords, or punctuation—that an AI language model can consider or “remember” at one time. Think of it as the model’s working memory, the active portion of text it analyzes when processing input and generating outputs. This window frames what the model can directly reference or draw upon during a single interaction, allowing it to maintain continuity and relevance within that scope.

How Context Windows Work

Language models process language through tokens, which break down text into manageable pieces, ranging from full words to smaller segments depending on the model’s design. The context window defines how many of these tokens the model can use simultaneously. As the model receives input, it incorporates all tokens within this window to understand meaning, context, and relationships before predicting the next token or generating a response. If the input exceeds the window size, the model must truncate or forget earlier tokens, limiting how much information it can actively consider at once.

Why Context Windows Matter in LLMs

The size of the context window directly influences a large language model’s performance and usefulness. A larger window allows the model to consider more text—whether it’s a long conversation, a detailed document, or complex instructions—leading to better understanding, more coherent outputs, and fewer hallucinations or errors caused by missing context. It also enhances the model’s ability to handle nuanced or extended interactions without losing track of earlier details. Consequently, innovations that expand context window sizes are crucial for improving how LLMs interact naturally and effectively across various applications.