What is a context window, and how do you design around it when your data is bigger than it?

The context window is the maximum number of tokens (input plus output) the model can attend to in a single request, and anything beyond it must be dropped or the call fails.

Even within the window, quality can degrade for information buried in the middle of a very long prompt, so 'it technically fits' is not the same as 'the model will use it well.' When data exceeds the window you do not just paste more; you retrieve the most relevant chunks (RAG), summarize older conversation turns, or split the task into multiple calls whose results you combine. A senior answer names the tradeoff: a larger window reduces engineering effort but costs more per call and can still lose the needle, so retrieval plus a modest window is often cheaper and more accurate than brute-forcing a huge window.