Interview Bank

How to reason about it · think before answering

1. The hinge is that uncertain and wrong are two different failures. Most candidates answer retry or escalate to a human, collapsing both into one. The discriminator is stating a value judgement before giving a policy.
2. The claim first: routing to the wrong sub-agent is far worse than failing to route. A failure announces itself and lets you ask a clarifying question. A wrong route does not — the receiving agent has no idea it got the wrong job and will produce a confident, well-formatted, wrong answer that the user will act on. A confident wrong answer costs a hundred times more than I did not catch that.
3. Then give a concrete policy with real numbers: if the model's confidence is below 0.6, or the route name is not in the allowed list, fall back to the small-talk agent and stamp the reason with a fallback prefix plus a cause code (low confidence, unknown route, invalid shape). The fallback agent's job is to ask for the one missing detail rather than guess — falling back means handing the uncertainty back to the user.
4. The threshold question is the real test, so do not recite a number: it depends on which error is more expensive. In customer support one extra question costs mild annoyance while a misroute can become a wrong refund promise, so stay conservative. For an internal tool the extra question is the bigger cost, so lower it. Then give a method: sweep thresholds over a golden set, plot misroute rate against clarification rate, and pick the knee.
5. Name the trap: the confidence number is self-reported and is not a probability. Nine tenths does not mean nine in ten are right. It is a usable ranking signal within one model and one prompt — good as a gate, useless for expected-value math. Real accuracy comes from offline evaluation.
6. Expect: does falling back just hide the problem? Not if you record cause codes. Group a week of fallbacks by cause and you can see exactly which intent the routing prompt fails to describe. The fallback stops the bleeding; the cause code is what fixes it.

分析过程 · 先想清楚再作答

1. 题眼在「不确定」和「错误」是两件事。多数人只答重试或人工接管，那是把两个问题揉成一个。区分度在于你能不能先给出一条价值判断，再给策略。
2. 先立论：**路由到错的子 Agent，比路由失败糟糕得多**。失败你至少知道自己失败了，可以追问一句；错了，接手的子 Agent 完全不知道自己接错了活，会用笃定的语气给出一个格式完整的错误答案，用户不会怀疑，会照着去操作。一个自信的错误答案比一句「我没听清」贵一百倍。
3. 再给可执行的策略，数字要具体：模型给的置信度低于 0.6，或者路由名不在合法名单里，一律落到兜底的 smalltalk，并在 routingReason 里打上 fallback 前缀加原因码（低置信度、未知路由、结构非法各一种）。兜底那位的人设是「信息不足先追问一句缺的关键信息，不要猜」——兜底的本质是把不确定性还给用户。
4. 阈值怎么定这一问是重点，别背数字：**取决于两类错误哪一类更贵**。客服场景里多问一句只是用户小小的不耐烦，派错可能变成一条错误的退款承诺，所以宁可保守取 0.6；内部工具型 Agent 里多问一句反而更烦人，阈值就该放低。再补一句可落地的定法：拿标准样本集扫一遍，画出不同阈值下的误派率与追问率，选拐点。
5. 必须点破的一个坑：**置信度是模型自己报的，它不是概率**。模型说 0.9 不代表有九成对。它只是同一模型、同一提示词下相对可用的排序信号，只能当闸门用，不能拿去算期望值。真正的准确率要靠离线评估去量。
6. 可以预期的追问：兜底会不会把问题掩盖掉？答案是不会，前提是你记了原因码——把一周内落进兜底的请求按原因分组，能直接看出分诊提示词缺了哪一类描述。兜底是止血，原因码才是治本的输入。

How to reason about it · think before answering

1. This tests whether you have thought about the direction of the error. A router is a classifier and classifiers misfire; "add more training data" is not a fallback design.
2. Break the errors down by direction — that is the backbone of the answer. Across three routes (direct answer, single-hop, multi-hop) the six confusions carry wildly asymmetric costs. Routing a retrieval-worthy question to a direct answer leaves the model with no material at all, so it fabricates: the most expensive error. Routing chit-chat to single-hop merely wastes one retrieval. Routing multi-hop to single-hop just yields an incomplete answer.
3. The conclusion follows: bias the fallback toward spending a little more, and default to single-hop retrieval whenever the classifier is unsure. Single-hop is the cheapest error to make, and it is recoverable — with partial material the model can still say it only found half the answer; with no material it can only invent one.
4. Add a runtime fallback, which beats better up-front classification: after a direct-answer routing, if the draft reply contains figures, amounts or dates that need a source, fall back to retrieval and answer again; after a single-hop routing, if no candidate clears the admission gate, escalate to multi-hop or abstain. Correcting the earlier decision with the later observation is the single most useful pattern in routing systems.
5. Mention observability: log every routing decision with the raw question, the label, and whether a fallback fired. Without that log you know neither how accurate the router is nor what to train the next version on.
6. Expect "when should you skip routing entirely?" When chit-chat is a small share of traffic and multi-hop questions are rare, the classification call costs more than it saves. In our 30-document lab the real gain from routing was not saved retrievals but the ability to give recognised multi-hop questions a larger context budget.

分析过程 · 先想清楚再作答

1. 这题在考「有没有想过错误的方向」。路由是分类器，分类器一定会错；只答「多加训练数据提高准确率」的，等于没回答兜底怎么设计。
2. 先把错误按方向拆开，这一步是整题的骨架：三条路（直接回答、单跳检索、多跳检索）两两误判，代价完全不对称。把该检索的判成直接回答，模型手里一点材料都没有，只能编，这是最贵的一种错；把闲聊判成单跳，只是白花一次检索；把多跳判成单跳，只是少查一轮、答得不全。
3. 结论顺势就出来了：**兜底方向要偏向「多花一点钱」，判不出来一律退回单跳检索。** 单跳是三条路里错得最轻的一条，而且它的错误是可恢复的——材料不全模型还能说「资料里只查到一半」，材料为空它就只能编。
4. 再补一层运行时兜底，比事前分类更管用：分类成直接回答之后，如果模型的回答里出现了具体数字、金额、日期这类需要出处的内容，就回退去检索一次再答；分类成单跳之后，如果检索侧一条都没过门槛，就升级走多跳或直接拒答。**用后一步的观测结果纠正前一步的判断**，这是路由系统最实用的一条设计。
5. 还要提一句可观测性：路由的每一次判定都要落日志，带上原始问题、判定结果、后续是否发生了兜底升级。没有这份日志，你既不知道路由准不准，也没法攒出下一版的训练集。
6. 可预期的追问是「什么时候干脆别做路由」。答：流量里闲聊占比很低、且多跳问题很少时，路由省下的钱还不够付分类调用的钱，这时候直接全部走单跳更划算——我们在 30 篇语料的实验里就看到，路由真正的收益并不在省检索，而在于认出多跳之后给它更高的上下文预算。