面试题库

分析过程 · 先想清楚再作答

1. 这题看着像闲聊，其实在考「你会不会做优先级」。面试官想听的不是勤奋，是判断：一周时间是固定的，你怎么决定把它花在哪。答「每天复习八小时，从头过一遍」就是没有判断。
2. 先给一条可复用的推导：复习的边际收益取决于「这一块你现在离能讲清有多远」，所以任何计划的第一步都必须是**测量**，而不是学习。没测量就排计划，等于闭着眼睛分配预算。
3. 落到具体做法：第一天只做分诊——把所有题目出声过一遍，按「能讲清 / 看一眼能讲 / 讲不出」标三种颜色，看到不会的立刻跳过。这一遍的产出是一张分布图，不是知识。第二、三天只碰后两类，第四天只碰仍然讲不出的，最后两三天留给模拟和表达。
4. 要主动说出「只标记不纠结」这条纪律和它的失败模式：碰到第一道不会的题当场去补，一道题吃掉半小时，做到第 20 道今天就没了，剩下的题连颜色都没有。这个细节最能证明你真的这样练过。
5. 再补一个判据：判断「会」的标准必须可证伪——出声、限时 90 秒、不看提纲。默读产生的流畅感是题库给的，不是你的。
6. 可预期的追问：如果分诊发现红题集中在同一块怎么办？答案是先补那一块的**上游**概念，而不是逐题补——同一块里的题往往共用一个没吃透的前置，补上游一道题能带亮五道。

How to reason about it · think before answering

1. This sounds casual but it tests prioritization. The interviewer wants judgment, not diligence: the week is fixed, so how do you decide where it goes? 'Eight hours a day, start from the top' shows no judgment at all.
2. Offer a reusable rule: the marginal value of reviewing a topic depends on how far you currently are from being able to explain it, so step one of any plan is measurement, not study. Planning without measuring is allocating a budget blindfolded.
3. Concretely: day one is triage only — say every answer out loud and tag it green (can explain unaided), yellow (can explain with a glance at notes), or red (cannot). Skip reds immediately. The output of that pass is a distribution, not knowledge. Days two and three hit yellow and red, day four hits what is still red, and the last days go to mock interviews and delivery.
4. Name the discipline and its failure mode: fixing the first red question on the spot burns thirty minutes, so by question twenty the day is gone and most of the set was never assessed. That detail is what proves you have actually done this.
5. Add a falsifiable bar for 'I know it': out loud, ninety seconds, no notes. The fluency you feel while reading silently belongs to the author, not to you.
6. Expect the follow-up: what if the reds cluster in one area? Fix the upstream concept first rather than the individual questions — clustered reds usually share one missing prerequisite, and repairing it lights up five questions at once.

分析过程 · 先想清楚再作答

1. 这题在考优先级判断，而不是知识面。答成「上重排、上混合检索、上查询改写」这类手法清单几乎必然掉分——因为它跳过了一个前提：**你凭什么知道这三件对你的系统有用？** 面试官等的就是这句话。
2. 所以第一件必须是**建评估**，而且理由要具体到不可反驳：没有秤，剩下两件做完你也说不清是变好还是变坏；有了秤，后面每一笔钱都能算回报。而且它便宜——检索侧三个指标是纯本地计算、几秒钟、零成本，能挂进每次提交；花时间的只是给题目标答案文档那一次。顺带说清评估集的配比：多跳与无答案各占一成以上，缺了无答案那一类，一个只会硬答的系统在报表上就是满分。
3. 第二件是**把拒答从提示词搬进代码**，这一件的性价比通常最高而最容易被跳过。提示词里写十遍「找不到就说找不到」增益接近于零；而引用编号是一个闭集，判它存不存在只要一行代码，再加一道「这句话与被引块的实质重合度」就能拦住「编号是真的、内容是假的」那一类。我们实验里的基线拒答率是 0.0%——四道语料里根本没有答案的题一道都没闭嘴，这类缺陷在只报召回率的报表上完全不可见。
4. 第三件要**先看失败案例再决定**，这才是这道题真正的答案。看完面板你会落到其中之一：多跳题占比高就补桥接检索或改索引结构；换个说法就捞不到，说明该上向量那一路或混合检索；答案捞到了却排不进上下文，那是重排或者预算的活。**先有失败案例，再有手法**——我们试过五种高级索引结构，没有一种跑赢基线，因为我们的系统压根没有那些结构要补的短板。
5. 为什么不选那些看起来更亮的：Agentic 检索的收益集中在多跳题上而代价摊给全部问题；「全开」所有查询侧手法在我们的实测里召回率和默认配置一模一样，模型调用却是 2.5 倍、检索次数 4.3 倍。**堆手法很容易，说清楚为什么关掉某几项才是本事。**
6. 可预期的追问：三件做完怎么证明钱花对了？答：每一项单独开关各跑一遍，报三笔账——指标涨了多少、延迟涨了多少、钱涨了多少。只报第一笔的提案不该被批准，包括你自己的。

How to reason about it · think before answering

1. This tests prioritisation, not breadth. Answering with a list of techniques — add reranking, add hybrid retrieval, add query rewriting — almost always loses points, because it skips a prerequisite: how do you know those three help your system? That is precisely the sentence the interviewer is waiting for.
2. So the first item has to be building evaluation, with a reason specific enough to be unarguable: without a scale, you cannot tell whether the other two helped or hurt; with one, every subsequent spend has a measurable return. It is also cheap — the three retrieval metrics are pure local computation, run in seconds, cost nothing, and can gate every commit; the only real effort is labelling answer documents once. Include the composition rule: multi-hop and unanswerable each above ten percent, because without the unanswerable class a system that only ever guesses scores perfectly on your report.
3. Second, move abstention out of the prompt and into code — usually the best return per unit of effort, and the item most often skipped. Writing 'say you don't know' ten times in a prompt buys almost nothing. Citation numbers are a closed set, so checking existence is one line, and adding a substantive-overlap check catches the harder forgery where the number is real but the content is not. Our baseline abstention rate was 0.0 percent: four questions with no answer in the corpus, zero of them declined — a defect that is completely invisible on a report that only shows recall.
4. Third, look at the failure cases before deciding, which is the actual answer to this question. After reading the panel you land on one of a few branches: a high share of multi-hop means bridging retrieval or a different index structure; queries that miss when phrased differently mean you need the vector route or hybrid retrieval; answers retrieved but never packed into context means reranking or budget. Failure cases first, technique second — we tried five advanced index structures and not one beat the baseline, because our system simply did not have the weakness they address.
5. Why not the flashier options: agentic retrieval concentrates its gains on multi-hop while spreading cost across every question, and in our measurements turning on every query-side technique produced exactly the same recall as the default configuration while using 2.5 times the model calls and 4.3 times the retrievals. Stacking techniques is easy; explaining why you switched several off is the skill.
6. Expected follow-up: once the three are done, how do you prove the money was well spent? Toggle each one individually and report three ledgers — how much the metric moved, how much latency moved, how much cost moved. A proposal that reports only the first should not be approved, including your own.