Interview Bank

Context Engineering in 5 Days

D1 Context Is the Scarcest Resource: the Window, Attention Decay, and Cost — From Prompt Engineering to Context Engineering

• What actually goes into the context of a single agent request, and which part is most likely to blow up?一次 Agent 请求的上下文里都有什么？哪一块最容易失控，为什么？

  Common in ChinaCommon overseasBasic#context-window#token-budget

  How to reason about it · think before answering

  1. This question separates people who have measured from people who have read. Naming the four parts is easy; describing how each one grows is where the signal is.
  2. Classify the four by growth pattern: system prompt and tool definitions are resent verbatim every turn at roughly constant size; conversation history grows linearly by tens of tokens per turn; tool results grow in steps, often thousands of tokens per call.
  3. Conclusion: tool results are the most likely to blow up, because a single increment is one to two orders of magnitude larger than the others and its size is decided by an external system you do not control. Tool definitions come second since they scale with a tool count that only ever goes up.
  4. Add the subtlety: tool results live inside user-role messages but they are data, not dialogue. Bucketing by message role folds them into history and ruins the breakdown, so bucket by content block type instead.
  5. Expect the follow-up: what numbers did you actually see? A concrete figure lands best, for example tool results at 72.9 percent of a customer-support session while most people had guessed history.

  分析过程 · 先想清楚再作答

  1. 这题在考你有没有真的量过。能背出「系统提示、工具定义、历史、工具结果」四块的人很多，能说出各自增长方式的人很少，区分度全在后半句。
  2. 怎么拆：按「每一轮会怎么变」给四块归类。系统提示和工具定义是每轮原样重发、长度基本不变；对话历史是线性增长，每轮加几十个 token；工具结果是阶梯增长，一次调用就能加两千。
  3. 结论：最容易失控的是工具结果，因为它的单次增量比其它三块大一到两个数量级，而且完全由外部系统决定，你写代码的时候看不到它会有多大。工具定义排第二，它随工具数量线性增长，而工具是最容易被顺手加上去的东西。
  4. 补一个容易被忽略的点：工具结果虽然写在 user 角色的消息里，但它是数据不是对话。按消息角色统计会把它算进历史，那张表就废了——要按内容块的类型拆。
  5. 可预期的追问：那你实际量出来是多少？给一个具体数字最有说服力，比如一次电商客服会话里工具结果占 72.9%，而大多数人事先都猜的是对话历史。

  Key points

  • Four parts: system prompt, tool definitions, conversation history, tool results.
  • Group them by growth: the first two are resent every turn at near-constant size, history grows linearly, tool results grow in steps.
  • Tool results blow up first because a single call can add thousands of tokens and its size is set externally; tool definitions are second, scaling with tool count.
  • Bucket by content block type, not by message role, or tool results get miscounted as history.

  答题要点

  • 四块：系统提示、工具定义、对话历史、工具结果。
  • 按增长方式分：前两块每轮重发且基本恒定，历史线性增长，工具结果阶梯增长。
  • 最容易失控的是工具结果，单次增量最大且由外部系统决定；其次是工具定义，随工具数量增长。
  • 统计时要按内容块类型拆，不能按消息角色拆，否则工具结果会被算进对话历史。