What is a context window? How do context windows work

A context window is the token-limited active workspace an LLM uses for a single request, encompassing system instructions, prompts, conversation history, retrieved documents, tool outputs, and generated responses, but not its training data. Larger windows allow models to examine longer documents, codebases, and conversations together, yet they increase computation, memory use, latency, and cost while not ensuring that all included information is used effectively. Research such as Lost in the Middle and RULER indicates that models often retrieve information most reliably near the beginning or end of long inputs and may overlook relevant details buried in the middle. Because tokenization varies by language and model, developers should measure actual usage rather than rely on word-count estimates. Effective context management therefore emphasizes selective retrieval through RAG, summarization of older conversations, pruning irrelevant history, structured memory, and caching repeated information. Model selection should consider not only advertised context capacity but also practical performance on realistic workloads, including reasoning quality, retrieval accuracy, response speed, output limits, and cost.

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