Interview Bank
328 questions total; 1 shown with current filters.
CourseAllFrom Frontend Engineer to Agent Engineer in 30 DaysPrompt Engineering From Scratch in 5 DaysMastering Claude: From Conversation to Claude Code in 5 DaysMastering Codex and the OpenAI Agents SDK in 5 DaysMCP in 7 Days: Wire Tools Into Any AgentAgent Skills in 7 Days: Turn Experience Into Reusable CapabilityContext Engineering in 5 DaysRAG in 14 Days: From Retrieval to Trustworthy AnswersBuild an AI Short-Drama Production Pipeline With Agents in 14 Days
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Context Engineering in 5 Days
D5 Measuring and Tuning: the Token Bill, Context Utilization, Failure-Mode Triage, and a Comprehensive Interview Deep Dive
How much context engineering is enough, and how do you know when to stop?上下文工程做到什么程度算够?你怎么知道该停手了?
Common in ChinaCommon overseasIntermediate#tuning#stopping-criteriaHow to reason about it · think before answering
- This is open-ended but has a clear right shape. Saying more optimization is always better reads as lacking cost awareness, because context work is unbounded and will be overdone without a stopping rule.
- Name the concrete cost of overdoing it rather than stopping at wasted time. Trim too hard and you cut fields needed later; compact too hard and you lose hard requirements that do not read like conclusions; cut tools too far and the agent cannot finish the task. None of these raise errors; they show up only in accuracy, the most expensive bill.
- Give at least three checkable stopping conditions: useful-token share stable in a healthy band such as above fifty percent with no headroom across several measurements; per-turn occupancy under fifty percent on your longest case; and the last change delivering less than a five percent bill reduction.
- Land on the third: a sub-five-percent gain means what remains is necessary overhead, and squeezing further trades accuracy for money. It matters most because it is the only condition that transfers across projects unchanged.
- Expect the follow-up on preventing regression. Freeze the measurement into a regression suite: fixed cases, rerun on every change, bill and both metrics under monitoring. Model upgrades, tool churn, and downstream field changes each degrade it again.
分析过程 · 先想清楚再作答
- 这题是开放题,但它有明确的好坏。答「越优化越好」的人会被判成没有成本意识,因为上下文工程是个能无限做下去的活,不定停手判据就一定会做过头。
- 怎么拆:先说清过度优化的具体代价,不要停在「浪费时间」。裁得太狠会把后面才用得上的字段裁掉,压得太狠会丢掉不像结论的硬性要求,工具裁得太少会让模型没法完成任务。这些都不报错,只在正确率上体现,而正确率是最贵的一笔账。
- 给可核对的停手条件,至少三条:有效信息占比稳定在一个合理区间(比如 50% 以上)且连续几次测量没有上升空间;最长那条用例上的单轮窗口占用率不超过 50%;最近一次改动带来的账单降幅低于 5%。
- 结论落在第三条:降幅低于 5% 说明剩下的都是必要开销,继续压就是在拿正确率换钱。这条比前两条更重要,因为它是唯一一条与具体项目无关、可以直接复用的判据。
- 可预期的追问:那怎么保证停手之后不退化?把这套度量固化成回归:一批固定用例、每次改动都重跑、账单与两个指标进监控。上下文工程不是一次性项目,模型换代、工具增减、下游接口改字段,任何一件都会让它重新变差。
Key points
- Overdoing it fails silently in accuracy: fields needed later get cut, hard requirements get summarized away, and too few tools leave the task unfinishable.
- Three stopping conditions: a stable useful-token share with no headroom, per-turn occupancy under fifty percent on the longest case, and a last change worth under five percent of the bill.
- The third transfers best: under five percent means what remains is necessary overhead and further squeezing trades accuracy for money.
- After stopping, freeze it into regression: fixed cases, rerun on every change, and monitor the bill plus both metrics.
答题要点
- 过度优化的代价不报错,只在正确率上体现:裁掉后面才用的字段、压掉不像结论的硬性要求、工具少到做不完任务。
- 三条停手判据:有效信息占比稳定且无上升空间、最长用例的单轮占用率不超过 50%、最近一次改动账单降幅低于 5%。
- 第三条最通用:降幅低于 5% 说明剩下的是必要开销,再压就是拿正确率换钱。
- 停手后要固化成回归:固定用例、每次改动重跑、账单与两个指标进监控。