Interview Bank

From Frontend Engineer to Agent Engineer in 30 Days

D16 Dynamic Routing With a Supervisor: Structured-Output Routing, Override, routingReason

• What is a field like routingReason actually worth in production? Is it just logging?routingReason 这类调试信息在生产系统里有什么价值？只是打日志而已吗？

  Common in ChinaCommon overseasIntermediate#observability#routing#debugging

  How to reason about it · think before answering

  1. This looks like a throwaway question but it screens for whether you have ever been on call. Anyone who stops at it helps with debugging has not.
  2. Start with the fact you cannot design around: the routing decision is made by a model, and models are not reproducible. The same sentence may be judged differently next time, so you cannot re-run to see what it was thinking. The reason must be captured at decision time or it is gone forever — that is what turns this field from a log line into the only audit evidence you have.
  3. Then give three concrete uses. One, it separates a wrong model judgement from a parsing or fallback problem, provided the prefix carries a cause code. Two, it is raw material for the next prompt revision: group a week of fallbacks by cause and the missing intent descriptions jump out. Three, it feeds offline evaluation — a golden set should score routing accuracy, not just the final answer, and that is only scorable if the decision and its reason were recorded.
  4. Mention the shape: a structured prefix wrapping a human sentence. The prefix (fallback plus cause, override plus target) is what you aggregate on; the sentence is what you read for one specific case. Making the whole field prose puts you right back in the failure mode this chapter argues against.
  5. Add the detail people skip: neither a fallback nor a human override should erase the model's original judgement — carry it into the reason. Otherwise nobody can later tell whether the model got it wrong or a human redirected it. Twenty extra characters save an afternoon of archaeology.
  6. Expect: do these fields create privacy or cost problems? Yes, so record the basis for the decision rather than the user's raw text, cap the length, and reuse the same run identifier as your tracing instead of inventing a parallel one.

  分析过程 · 先想清楚再作答

  1. 这题看着像水题，其实在筛「有没有真的排查过线上问题」。答「方便调试」就结束的人，基本没值过班。
  2. 先给一条不可回避的事实：**路由决策是模型做的，而模型不可复现**。同一句话下次未必给同样的判断，你没法重跑一遍去看「当时是怎么想的」。所以理由必须在当时就写下来，否则那次判断永远丢了。这一条把 routingReason 从「日志」抬到了「唯一的审计证据」。
  3. 然后给三个具体用途，每个都要能落地：一是把「模型判错了」和「解析或兜底出错了」分开，前缀写成 fallback 加原因码，一眼就能分辨；二是攒下一版提示词的素材，把一周内落进兜底的请求按原因分组，会看到集中的几类意图缺描述；三是它是离线评估的输入——标准样本集要评的不只是最终回答，还有分诊准不准，而这件事只有当时记了判断和理由才评得了。
  4. 写法上有个细节值得主动说：**结构化的壳加自然语言的芯**。前缀（fallback 加原因、override 加目标）用来聚合统计，后面那句人话用来看具体这一单。整条都写成自然语言，就退回成本章批判的那种东西了。
  5. 再补一条容易被忽略的：兜底和人工改派都不要擦掉模型的原判，原样拼进理由里。否则一周后没人说得清这一单是模型判错了还是本来就被人改过——多写二十个字符，省掉一次翻遍代码的排查。
  6. 可以预期的追问：这些字段会不会带来隐私或成本问题？答案是会，所以理由里只写判断依据不写用户原文，长度设上限（比如 120 字），并且和链路追踪共用同一个 run 标识，别另起一套。

  Key points

  • The decision comes from a model and is not reproducible, so the reason must be captured at decision time — it is the only audit evidence you get
  • Use one: it separates a wrong model judgement from a parsing or fallback failure, via a cause code in the prefix
  • Use two: grouping a week of fallbacks by cause tells you exactly what the next routing prompt is missing
  • Use three: it feeds offline evaluation, since routing accuracy can only be scored if the decision and reason were recorded
  • Shape it as a structured prefix around a human sentence: aggregate on the prefix, read the sentence for one case
  • Keep the model's original judgement through fallbacks and overrides; store the basis rather than raw user text, cap the length, and reuse the tracing run id

  答题要点

  • 路由决策由模型做出且不可复现，理由必须在当时写下来，否则那次判断永远丢了——它是唯一的审计证据
  • 用途一：把「模型判错」和「解析或兜底出错」分开，靠 fallback 加原因码一眼分辨
  • 用途二：把一周内落进兜底的请求按原因分组，直接得到下一版分诊提示词该补什么
  • 用途三：它是离线评估的输入，分诊准确率这个指标只有记了当时的判断与理由才评得了
  • 写法是结构化的壳加自然语言的芯：前缀用于聚合统计，人话用于看具体这一单
  • 兜底与人工改派都要保留模型原判；理由只写判断依据不写用户原文，长度设上限，并复用链路追踪的 run 标识

MCP in 7 Days: Wire Tools Into Any Agent

D7 Productionizing and Retrospective: Writing Evals for Tools, Versioning, Publishing to npm and a Registry, Observability, and a Capstone Project

• A user reports that one of your MCP tools is 'always getting it wrong' in production. In what order do you investigate?线上有人反馈某个 MCP 工具「总是调不对」。你按什么顺序排查？

  Common in ChinaCommon overseasIntermediate#observability#debugging

  How to reason about it · think before answering

  1. This tests ordering, not breadth. Anyone who opens with logs and stack traces gets asked how they know the problem is server-side at all.
  2. Step zero is translating 'getting it wrong' into three mutually exclusive symptoms, without which everything after is guesswork: it was never called, it was called with wrong arguments, or it was called correctly and returned the wrong thing. Asking whether it did nothing or did the wrong thing, or simply checking whether a call was logged, separates them.
  3. Each symptom has its own path. Never called means the description is at fault: run the eval set and see whether happy paths or edges dropped, since happy-path drops mean a vague description and edge drops mean the sentence distinguishing similar tools is missing. Wrong arguments means the schema is at fault: ambiguous field names, unstated formats, wrong required markers. The signal is a persistently high tool-execution error rate, which usually means the schema is unclear rather than the model being dumb. Only a wrong result is a code problem, and only then do unit tests and logs matter.
  4. State the metric ordering too: error rate first, selection accuracy second. A normal error rate with unhappy users almost always means the tool is not being chosen; a spiking error rate sends you to the code and upstream. Watch P95, not the mean, because a remote tool degrading from 200 milliseconds to 8 seconds barely moves an average.
  5. Volunteer two commonly missed causes. Aggregation collisions: the client is connected to several servers, two tools share a name, and the model picked the other one, so your server was never called and investigating it will never find anything. And version or caching: list results carry ttlMs cache hints, so the client may hold a stale tool list, and refresh depends on a listChanged notification.
  6. Conclusion: the chain has four links, description, schema, aggregation and caching, and implementation. Walk it in the order the model sees it, because the earliest links produce no error logs and are therefore the ones people skip.
  7. Likely follow-up: how do you keep evidence? Emit one structured log per call with tool name, an argument digest plus field names, the isError flag, duration, and whether a human confirmed the call, that last column being the only way to distinguish user intent from the model acting on its own.

  分析过程 · 先想清楚再作答

  1. 这题考的是排查的**顺序**，不是知识点的多少。上来就贴日志和堆栈的人会被追问「你怎么知道问题在服务端」。
  2. 第零步是把「调不对」翻译成三种互斥的现象，这一步不做后面全是猜：一是**没被调**（模型压根没选这个工具）；二是**调了但参数错**；三是**调了参数也对，但结果不对**。问一句「那次它是没动，还是动了但做错了」，或者直接去日志里看有没有这条调用记录，就能分开。
  3. 对应三条不同的路。没被调，问题在**描述**：去跑评估集，看正例还是边界掉了；正例掉说明描述写糊，边界掉说明缺了区分相似工具的那句话。参数错，问题在 **schema**：看字段名是不是有歧义、描述里有没有写清格式、必填项是不是标对了；这类问题的信号是错误率里工具执行错误持续偏高——那通常不是模型笨，是 schema 没说清。结果不对才是代码问题，这时候才轮到单元测试和日志。
  4. 指标层面的顺序也说一下：**先看错误率，再看选中率**。错误率正常但用户说不好用，八成是选不中；错误率飙了才去看代码和上游。耗时看 P95 不看平均值，远程服务端上一个工具从 200 毫秒退化到 8 秒，平均值可能只动一点点。
  5. 还有两条容易被忽略但很常见的原因，要主动提。一是**聚合冲突**：客户端连了多个服务端，两个工具重名，模型选中的是另一个服务端的那个——这时候「你的工具」根本没被调，查你的服务端永远查不出来。二是**版本或缓存**：列表结果带 ttlMs 缓存提示，客户端可能拿着旧的工具清单；工具清单变了要靠 listChanged 通知才会重新拉。
  6. 结论：这条链上有四个环节——描述、schema、聚合与缓存、实现。**按模型看得见的顺序从前往后查**，因为越靠前的环节越不产生错误日志，也就越容易被跳过。
  7. 可预期的追问：怎么留证据？每次调用记一条结构化日志，字段里要有工具名、参数摘要与字段名、是否 isError、耗时、以及这次调用有没有经过人工确认；最后那一栏是事后区分「用户授意」和「模型自作主张」的唯一依据。

  Key points

  • First split 'getting it wrong' into never called, wrong arguments, or wrong result; the split decides where to look
  • Never called points at the description and the eval set; wrong arguments at the schema; only a wrong result at the code
  • Check error rate before selection accuracy, and read P95 rather than the mean
  • Do not miss aggregation collisions, where another server's same-named tool was chosen, or a stale cached tool list

  答题要点

  • 先把「调不对」分成没被调、参数错、结果错三种互斥现象，再决定查哪里
  • 没被调查描述并跑评估集；参数错查 schema；结果错才轮到代码与日志
  • 指标顺序是先错误率再选中率；耗时看 P95 不看平均值
  • 别漏掉聚合重名（选中的是别的服务端的同名工具）和工具清单缓存这两类原因

RAG in 14 Days: From Retrieval to Trustworthy Answers

D1 Why Retrieve at All: Hallucination, Knowledge Cutoffs, and the Cost of Long Context; a Minimal Keyword-Only RAG

• A retrieval-augmented generation system gave a wrong answer. How do you determine whether retrieval or generation is at fault?一个检索增强生成系统答错了，你怎么定位是检索的锅还是生成的锅？

  Common in ChinaCommon overseasIntermediate#debugging#failure-modes#evaluation

  How to reason about it · think before answering

  1. The question asks how you localise the fault, not what the possible causes are. Listing causes loses; the interviewer wants an ordered procedure that ends in concrete actions.
  2. Give the cheapest first step: print the retrieved passages verbatim and read them. If the correct answer is not in there, retrieval is at fault. If it is in there and the model ignored it, generation is at fault. Thirty seconds, and it removes most of the guesswork.
  3. Then lay out the five stages — chunking, indexing, retrieval, context assembly, generation — with the rule: diagnose right to left, fix left to right. You see the generated answer first, but an error on the left is amplified by everything to its right.
  4. Add symptoms that pin down a stage: half-correct answers usually mean a rule was split across chunks; obviously irrelevant hits usually mean dirty parsing; the model ignoring the supplied material usually means the prompt never said it must; citation numbers that do not match their content point at generation.
  5. Land it in engineering terms: to run this procedure repeatedly you must log the retrieved hits, the passages that entered the context, and the final answer together, otherwise production issues are unreproducible. At scale this becomes a fixed question set with metrics rather than case-by-case reading.
  6. Expected follow-up: if retrieval missed the document, will prompt tuning help? No. Nothing in the prompt can conjure material that was never supplied.

  分析过程 · 先想清楚再作答

  1. 题眼在「怎么定位」，不在「有哪些原因」。答成一串可能原因的罗列就输了，面试官想听的是一个有先后顺序、能落到具体动作的排查流程。
  2. 先给最省时间的第一步：把这次检索出来的几段原文原样打印出来，自己读一遍。正确答案不在里面就是检索的锅，在里面而模型没用上才是生成的锅。这一步三十秒，能省掉大半天的瞎猜。
  3. 然后把链路展开成五个环节——切块、建索引、检索、组装上下文、生成——并给出「排查从右往左、修复从左往右」这条口径：从右往左是因为你最先看到的是生成结果，从左往右是因为左边的错会被右边放大。
  4. 补充几个能把环节钉死的症状：答案「半对」多半是切块把一条完整规则切断了；检索结果里混着一眼不相干的东西多半是解析没做干净；模型无视材料用先验知识作答，通常是提示词里少了「只能依据资料回答」；引用编号和内容对不上，那是生成侧漏读或串了行。
  5. 最后落到工程做法：这套排查要能重复做，就必须把每次请求的检索结果、进上下文的段落、最终回答一起记下来，否则线上出问题时你根本复现不了。到了要批量做的时候，就得换成一批固定问题加指标，而不是一条条人工看。
  6. 可预期的追问：如果检索确实没捞到，改提示词有没有用？答案是没用——材料里没有的东西，再好的指令也只能换一种编法。这句话最能证明你分清了两层。

  Key points

  • Always start by printing the retrieved passages and checking whether the correct answer is present at all.
  • Split the pipeline into chunking, indexing, retrieval, context assembly and generation; diagnose right to left, fix left to right.
  • Use symptoms to pin the stage: half-correct answers point at chunking, irrelevant hits at parsing, ignored material at the prompt, mismatched citations at generation.
  • If retrieval missed the document, prompt changes cannot help; the material simply is not there.
  • Log retrieved hits, the passages that entered the context, and the final answer together, or production failures are unreproducible.

  答题要点

  • 第一步永远是把检索出来的原文打印出来读一遍，判断正确答案在不在里面。
  • 把链路拆成切块、建索引、检索、组装上下文、生成五个环节，排查从右往左、修复从左往右。
  • 用症状钉环节：半对多半是切块问题，混入无关结果多半是解析问题，无视材料多半是提示词缺约束，引用与内容对不上是生成问题。
  • 检索没捞到时改提示词没有意义，材料里没有的东西模型只能编。
  • 要能重复排查就必须把检索结果、进上下文的段落和最终回答一起记录下来。

D14 Capstone Project and Retrospective: A Multi-Tenant Enterprise Knowledge-Base Q&A, a RAG Decision Map, and an Interview Deep Dive

• Users report that your live RAG system 'answers inaccurately'. What is your triage order?RAG 系统上线后用户反馈「答得不准」，你的排查顺序是什么？

  Common in ChinaCommon overseasIntermediate#debugging#failure-modes#observability

  How to reason about it · think before answering

  1. This one is almost guaranteed to be asked, and most people answer with a flat list of possibilities: maybe chunking, maybe the prompt, maybe the model. A list is not triage. Triage means an order, a decision rule at each step, and each step eliminating half the search space.
  2. First decompose the complaint. 'Inaccurate' hides at least four distinct failures whose fixes do not transfer: off-topic answers, partial answers, misaligned citations, and stale content. So the first action is not to change a setting, it is to obtain the specific question and answer and classify it into one of those four.
  3. Then give the order along with its justification: read the pipeline right to left, fix it left to right. Right to left because the generated answer is what you see first; left to right because upstream errors are amplified downstream — no prompt can recover a document retrieval never fetched. Concretely: dump the candidate pool and the final context for that question, and check whether the answer document is in the pool at all. Absent means a retrieval debt; present but below the admission gate means a gate debt; admitted but never packed into the context budget means chunks too large or budget too small; all present and still unused means it is finally a generation problem.
  4. One detail worth volunteering because it is easy to get wrong: for multi-hop questions, diagnose the documents that are missing, not whether any one of them was retrieved. In our experiment one question needed two documents; the first ranked first every time and the second never entered the candidate pool at all. Judging by 'any of them' labels it a budget problem, and you can spend a full day tuning budgets to no effect. This distinction only occurs to someone who has actually triaged question by question.
  5. The fourth class, stale content, happens outside the question path and has its own rule: first check whether reconciliation even noticed the edit (was the content hash computed after line-ending normalization?), then check whether the cache key includes the index version and the permission scope. 'When must this expire' is equivalent to 'is that thing part of the key' — leave something out of the key and changes to it will never invalidate the entry.
  6. Expected follow-up: how do you stop relying on manual triage? Build the classification into the evaluation panel so every missed question is automatically labelled with one of the four classes, and report it per tenant. A global average dilutes one customer's collapse across the whole population, and that customer is exactly the one who will file the complaint.

  分析过程 · 先想清楚再作答

  1. 这题几乎是必考题，而绝大多数人答成一堆并列的可能性：可能是切块问题、可能是提示词问题、可能是模型不行。并列不是排查，排查的意思是**有顺序、有判据、每一步能把可能性砍掉一半**。
  2. 先把「答得不准」这四个字拆开——它至少塞了四种病，而且修法互不通用：答非所问、只答得出片段、引用错位、更新不生效。所以第一个动作不是改配置，是**拿到具体的问题和回答，把它归到这四类里的一类**。
  3. 然后给顺序，而且要说清顺序的理由：**排查从右往左看、修复从左往右修**。从右往左是因为你最先看到的是生成结果；从左往右是因为上游的错会被下游放大——检索没捞到的东西，再好的提示词也救不回来。具体走法是：打印这一问的候选池和最终上下文，先看答案文档在不在候选池里。不在，是检索的债；在候选池但没过准入门槛，是门槛的债；过了门槛却没装进上下文预算，是块太大或预算太小；都进了而模型没用上，才轮到生成侧。
  4. 这里有一个容易写错的细节值得主动讲：**多跳题的诊断对象是缺的那几篇，不是「有没有捞到任意一篇」**。我们实验里有一道题要同时命中两篇，第一篇稳稳排第一、第二篇一次都没进候选池；用「任意一篇」去判会把它归成预算问题，然后你去调预算，调一整天也没用。这一条区分度很高，因为它只有真的按题排查过才想得到。
  5. 第四类「更新不生效」发生在问答之外，判据是另一条：先看对账认没认出这篇改了（内容指纹算之前有没有做换行归一化），再看缓存的 key 里有没有把索引版本和权限范围算进去。「什么时候必须失效」等价于「key 里有没有把那样东西算进去」，key 少放一样，那样东西变了缓存就不会失效。
  6. 可预期的追问：怎么让这套排查不靠人肉？答案是把分类做进评估面板——每一道没中的题自动标出它属于四类中的哪一类，并按租户分开统计。全局平均会把单个客户的塌方按人头摊薄，而线上会投诉的恰恰是那个客户。

  Key points

  • Classify the complaint into four failures first — off-topic, partial, misaligned citation, stale — because their fixes do not transfer.
  • Read right to left, fix left to right: dump the candidate pool and final context and find which layer the answer document stalls at.
  • The four rules in order: never retrieved, retrieved but below the gate, admitted but squeezed out of the budget, packed but unused by the model.
  • For multi-hop, diagnose only the missing documents; judging by 'any one retrieved' mislabels a never-retrieved case as a budget problem.
  • For stale content, check reconciliation and the cache key: what must expire is exactly what the key must contain.

  答题要点

  • 先把「答得不准」归类成四种病：答非所问、只答得出片段、引用错位、更新不生效——修法互不通用。
  • 排查从右往左看、修复从左往右修：先打印候选池与最终上下文，看答案文档卡在哪一层。
  • 四层判据依次是：没进候选池、进了没过门槛、过了没装进预算、都进了模型没用上。
  • 多跳题只诊断缺的那几篇；用「有没有捞到任意一篇」会把「根本没捞到」误判成预算问题。
  • 「更新不生效」查对账与缓存 key：什么时候必须失效，等价于 key 里有没有算进那样东西。

Build an AI Short-Drama Production Pipeline With Agents in 14 Days

D6 The Editing Bay: Assembling Footage Into One Vertical Cut With ffmpeg

• An auto-generated episode comes out with audio and video out of sync. What is your debugging order, and why that order?一集自动生成的短剧成片出现音画不同步，你的排查顺序是什么？为什么是这个顺序？

  Common in ChinaCommon overseasIntermediate#debugging#av-sync#timeline

  How to reason about it · think before answering

  1. The question is about ordering, not about listing causes. The interviewer wants to see you rank checks by hit rate divided by cost, not enumerate everything you can think of.
  2. Ask yourself first: where does time come from in this pipeline? If the answer is 'a structured timeline table', then step one is comparing planned durations in that table against the real durations of the media files. Highest hit rate, lowest cost, one ffprobe call.
  3. Step two is the upstream artifacts: when the voice track is longer than the shot, the line gets cut off. It sounds almost identical to drift but the root cause is different, and it should have been caught with a warning when the timeline was built.
  4. Step three is the compose stage: stream-copy concatenation requires identical parameters across segments, and misaligned timestamps shift things; adding crossfades shortens the final cut, so subtitles drift progressively unless their timecodes are recomputed.
  5. Also mention a general move: when all three fail, stop staring at the final cut and play the normalized per-shot segments to narrow the problem to one shot. Always shrink the search space before guessing.
  6. Expect the follow-up 'how do you stop relying on human ears'. Answer: assert at timeline-build time when planned and actual durations diverge beyond a threshold, and automatically verify that the final cut's duration matches the timeline total.

  分析过程 · 先想清楚再作答

  1. 这题的题眼在「顺序」两个字，不在「有哪些原因」。面试官想看的是你会不会按「命中率乘以排查成本」来排，而不是把想到的原因罗列一遍。
  2. 先问自己一个问题：这条流水线上，时间是从哪里来的？如果答案是「一张结构化的时间轴表」，那么第一步必然是拿表里的计划时长和素材文件的真实时长去对——这一步命中率最高、成本最低，一条 ffprobe 就能查完。
  3. 第二步查上游的产物本身：配音时长超过镜头时长时，台词会被截断，听感和不同步几乎一样，但根因完全不同。这类冲突应该在生成时间轴时就打警告，而不是留到成片阶段靠耳朵发现。
  4. 第三步才查合成环节：流拷贝拼接要求各段参数一致，时间戳对不齐就会错位；加了转场则成片整体变短，字幕若没跟着重算，表现为越到后面偏得越多。
  5. 还有一条通用招式值得说出来：三步都查不出来时，不要在成片里死磕，去播归一化之后的单镜片段，把问题缩小到某一镜身上。排查多段合成的问题永远优先缩小范围。
  6. 可预期的追问是「怎么让这类问题不再靠人耳发现」。答：在时间轴生成阶段加断言（计划时长与素材真实时长的偏差超过阈值就失败），并把成片时长与时间轴总时长的一致性做成自动校验。

  Key points

  • Start with the timeline table: compare planned durations against the media files' real durations. Highest hit rate, cheapest check.
  • Then check whether the voice track exceeds the shot duration and truncates the line. That should be warned about at timeline-build time.
  • Only then look at compose: concat method, timestamp alignment, and crossfades shortening the cut without recomputed subtitle timecodes.
  • General move: play the per-shot normalized segments to isolate one shot instead of guessing on the final cut.
  • Long term, turn duration consistency into assertions and automated checks rather than relying on ears.

  答题要点

  • 先查时间轴表里的计划时长与素材真实时长是否一致，这一步命中率最高、成本最低。
  • 再查配音是否超出镜头时长导致台词被截断，这类问题应在生成时间轴时就报警告。
  • 最后查合成环节：拼接方式、时间戳对齐、转场是否让成片变短而字幕没重算。
  • 三步之外的通用招式：播单镜片段把问题缩小到某一镜，不要盯着最终产物猜。
  • 长期方案是把时长一致性做成断言与自动校验，不靠人耳兜底。