面试题库

Claude 高效使用：从对话到 Claude Code

D2 长文档、多模态与 API 初见：大上下文怎么用、prompt caching 省钱、PDF 与图片输入、带引用回答；Messages API 最小调用

• 为什么说上下文窗口是 LLM 应用里最稀缺的资源？窗口已经有一百万 token 了，这个说法还成立吗？Why is the context window called the scarcest resource in LLM applications? With million-token windows, does that still hold?

  国内高频海外高频基础#context-window#cost

  分析过程 · 先想清楚再作答

  1. 题眼在第二句。只答「窗口有上限」已经过时了，面试官想听的是「窗口变大之后为什么还稀缺」。
  2. 从三条因果链推：一、模型无状态，每次请求都把全部输入重读一遍，输入 token 按次计费——窗口大只解决了放得下，没解决每次都要重搬；二、上下文越长，延迟越高、注意力越稀释，模型对早期指令的遵守度会下降，也就是「性能随填充度下降」；三、Agent 场景里每读一个文件、每跑一条命令的输出都进同一个窗口，填得比聊天快得多。
  3. 结论：窗口大了，稀缺性从「放不下」变成了「每一 token 都在花钱和稀释注意力」，所以管理手段变成了主动管：只放必要的、把不变的缓存起来、把查资料的活派给独立上下文的子代理、该清就清。
  4. 生产视角：算一笔账——60 页 PDF 约 10 万 token，围着它问 10 个问题就是 100 万输入 token；不用缓存和不用缓存的差价是一个量级。
  5. 可预期的追问：那什么时候应该让上下文积累？在一个复杂问题里深挖时历史是有价值的；判据是「这段历史下一步还会不会用到」。

  How to reason about it · think before answering

  1. The second sentence is the point. 'The window has a limit' is a dated answer; explain why scarcity survives large windows.
  2. Three causal chains: models are stateless so every request re-reads the whole input and bills it, a big window only solves fitting, not re-sending; longer context means more latency and diluted attention, so adherence to early instructions degrades as the window fills; and in agent workflows every file read and command output lands in the same window, filling it far faster than chat does.
  3. Conclusion: scarcity shifted from 'won't fit' to 'every token costs money and attention', so the discipline becomes active management — include only what is needed, cache the stable prefix, delegate research to subagents with their own context, and clear between tasks.
  4. Production math: a 60-page PDF is roughly 100k tokens; ten questions about it are a million input tokens; caching versus not caching is an order of magnitude apart.
  5. Follow-up: when should context accumulate? While deep in one complex problem where the history is still load-bearing; the test is whether the next step will use it.

  答题要点

  • 模型无状态，每次请求重读全部输入并计费；窗口大只解决放得下，不解决每次重搬
  • 上下文越长延迟越高、注意力越稀释，早期指令遵守度下降
  • Agent 场景每次读文件、跑命令的输出都进窗口，填得比聊天快得多
  • 对策：只放必要的、缓存不变前缀、用子代理隔离查资料、任务之间清空

  Key points

  • Models are stateless: every request re-reads and bills the full input; a large window solves fitting, not re-sending
  • Longer context raises latency and dilutes attention; adherence to early instructions drops
  • Agent workflows dump every file read and command output into the same window
  • Tactics: include only what's needed, cache the stable prefix, isolate research in subagents, clear between tasks