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    Basics

    What does “Context” mean?

    A beginner-friendly (and pro-useful) explanation of context windows, tokens, and how to avoid truncation.

    Basics
    ≈ 8 min read

    Summary (TL;DR)

    A model’s context window is its maximum working memory for a single request. It is measured in tokens and includes: your prompt + the chat history we send + the model’s output. Bigger context helps with long documents and long chats—but it also costs more and still requires good structure.

    Beginner: Context window in plain English

    When you talk to an AI model, it doesn’t remember everything forever. For each request, it only “sees” a limited amount of text. That limit is called the context window.

    The limit is measured in tokens (roughly pieces of words). If you exceed the limit, older parts are truncated or the model may refuse—so it can miss important details.

    Context = maximum tokens per request (input + history + output).
    Longer context helps with big documents and long conversations.
    If you hit the limit, earlier information can drop off (truncation).

    Pro: How to work well within a context window

    Long context is powerful, but you still need to manage it. These patterns help you keep quality high and cost predictable:

    Budget tokens: reserve space for the model’s answer (don’t spend 95% of the window on pasted text).
    Summarize progressively: keep a running, structured summary instead of re-sending the full history.
    Chunk large docs: ask for section-by-section extraction, then synthesize.
    Use retrieval (RAG): store documents in a knowledge base and only inject relevant excerpts.
    Pin critical facts: keep requirements, constraints, and definitions in a short “always include” block.

    Privacy note

    A larger context window means more text can be included in a single request. If you handle sensitive data, always prefer privacy-first setups and policies (e.g., ZDR) and minimize what you send.

    Sources / further reading

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