How Context Windows Shape What AI Language Models Can Remember and Process

A context window defines the maximum number of tokens a language model can process at once, encompassing the system prompt, conversation history, and generated output. The model uses a key-value cache during inference to store processed token data, enabling faster text generation without reprocessing the entire input each time. When a conversation exceeds the context limit, the earliest messages are dropped, causing the model to lose track of prior information. Performance can degrade even before the window is full, as models tend to overlook details buried in the middle of long prompts. Popular models like GPT-4 Turbo support up to 128,000 tokens, but real-world reliability often diminishes well before that ceiling is reached.