面试题库

14 天 RAG：从检索到可信回答

D4 切块策略：固定、递归、按结构、父子与语义五种切法，以及用评估而不是直觉来选

• 语义切分比递归切分贵不少，你怎么向团队证明这笔钱值得花？Semantic chunking costs considerably more than recursive splitting. How would you prove to your team that the money is well spent?

  国内高频海外高频深入#chunking#evaluation#cost

  分析过程 · 先想清楚再作答

  1. 这题表面问技术，实际考的是你会不会做一次带对照组的技术论证。上来就讲语义切分原理的人，答的是另一道题。
  2. 第一步是先承认它可能不值。语义切分的收益来自「文档没有可用的结构」；如果知识库是结构良好的文档，作者的标题层级已经免费替你做完了语义切分，这时候花的钱大概率打水漂。**先说清适用前提，再谈证明，这一步就把大多数候选人区分开了。**
  3. 第二步是把「值不值」翻译成可测的三笔账：指标涨了多少（同一批标准问题、同一个 token 预算下的命中率）、延迟涨了多少（切块是离线的，但更新链路的端到端时间会变）、钱涨了多少（首次全量 embedding 的费用，加上按更新频率折算的重算费用）。只报第一笔的论证不成立。
  4. 第三步是设计对照。递归切分是基线，语义切分是实验组，两组必须用同一份语料、同一批问题、同一个上下文预算、同一个检索器，只改切法这一个变量。改两个变量的实验，结论一文不值。
  5. 第四步是给决策一个门槛，而不是给一个感想。比如：命中率相对基线提升低于三个百分点就不上；提升超过五个百分点且重算成本在月度预算内就上；中间地带先在一类文档上灰度。**门槛要在跑数字之前定好**，否则你会不自觉地去迁就已经跑出来的结果。
  6. 可预期的追问是「有没有更便宜的办法拿到同样的收益」。答有：先试按结构切，它零成本且效果常常接近；结构确实不可用时，再考虑只对高价值的那一部分文档做语义切分，而不是全量上。

  How to reason about it · think before answering

  1. This looks like a technical question but it tests whether you can run a controlled technical argument. Launching into how semantic chunking works answers a different question.
  2. Step one is to concede that it may well not be worth it. The gain comes from documents that have no usable structure; if your knowledge base is well-formed documents, the authors' heading hierarchy already did the semantic split for free and the money is likely wasted.
  3. Step two is translating 'worth it' into three measurable numbers: how much the metric moved (hit rate on the same golden set under the same token budget), how much latency moved (chunking is offline, but the end-to-end update path changes), and how much it costs (the initial full embedding pass plus recomputation amortised over update frequency).
  4. Step three is the control. Recursive splitting is the baseline, semantic chunking the treatment, and they must share the corpus, the questions, the context budget and the retriever. Change one variable only; a two-variable experiment proves nothing.
  5. Step four is a decision threshold rather than an impression. For example: below three points of hit-rate gain, no; above five points with recomputation inside the monthly budget, yes; in between, roll it out on one document class first. Fix the threshold before you run the numbers, or you will quietly bend it to fit them.
  6. Expect the follow-up: is there a cheaper way to the same gain. Yes — try structural splitting first, since it is free and often nearly as good, and if the structure really is unusable, apply semantic chunking only to the high-value subset rather than the whole corpus.

  答题要点

  • 先讲适用前提：语义切分的收益来自文档没有可用结构，结构良好的文档上它大概率不值。
  • 把「值不值」翻译成三笔账：命中率涨多少、延迟涨多少、钱涨多少，只报第一笔不算论证。
  • 做对照实验：同语料、同问题集、同上下文预算、同检索器，只改切法一个变量。
  • 决策门槛必须在跑数字之前定好，避免事后迁就结果。
  • 先试零成本的按结构切；确需语义切分时也优先只覆盖高价值文档，而不是全量上。

  Key points

  • Start with the precondition: the gain comes from documents without usable structure, so on well-formed documents it usually is not worth it.
  • Translate 'worth it' into three numbers — hit rate, latency, and cost. Reporting only the first is not an argument.
  • Run a controlled comparison: same corpus, same golden set, same context budget, same retriever, with the splitting strategy as the only variable.
  • Fix the decision threshold before running the numbers so you cannot bend it to fit the result afterwards.
  • Try free structural splitting first, and if semantic chunking is genuinely needed, apply it to the high-value subset rather than the entire corpus.

D6 生成这一侧：上下文怎么排、引用怎么标、什么时候必须拒答，以及流式回答

• 知识库问答的拒答阈值怎么定？定高了和定低了各自的代价是什么？How do you set the refusal threshold for a knowledge-base assistant, and what does it cost you when the threshold is too high or too low?

  国内高频海外高频深入#refusal#thresholds#evaluation

  分析过程 · 先想清楚再作答

  1. 这题真正在考的是：你有没有意识到拒答不是一个阈值，而是好几条判据；以及你定阈值靠不靠数据。只谈一个分数阈值的回答，说明只做过最浅的一层。
  2. 先把拒答拆成三条线，它们的触发时机完全不同。检索分数太低：生成之前就能判，省一次模型调用。材料互相矛盾：也在生成之前判，代码在块之间找同一件事的不同数字，检出后要么并列两种说法与各自的更新日期，要么在有权威信号（比如一份点破了这条不一致的会议纪要）时按更新日期择一——选哪条是产品决策，但无论如何不能让模型自己悄悄挑一个。问题超出材料覆盖范围：只能在生成之后判，判据是跑完引用校验一条有效引用都没有。
  3. 强调三种话术必须不同。第一种要说「库里没有相关材料，换个说法或确认资料是否入库」，第三种要说「找到了相关文档但里面没有能直接回答的内容」——用户的下一步动作完全不同，混成一句「抱歉我不知道」等于把信息扔了。
  4. 再答代价这一半。定高了：能答的问题被挡在门外，用户看到查不到而材料其实在库里，这是最伤信任的一种错，而且它在日志里几乎不可见。定低了：低分噪声材料进上下文，模型拿着不相关的东西硬答，错误反而更隐蔽，因为回答看起来还带着引用。
  5. 怎么定：拿一批已知有答案和已知没答案的问题跑一遍，看两组的分数分布在哪里分开，按你更怕哪种错来取点。分数是没有绝对量纲的，换语料、换检索方式都要重定，所以真正要交付的是这套定阈值的流程，不是那个数字。
  6. 可预期的追问：单一分数阈值不够怎么办？答案是加判据而不是调数字——最高分与次高分的差、命中块数、以及生成后的引用校验结果，都是比原始分数更稳的信号。

  How to reason about it · think before answering

  1. What is really being tested: do you know that refusal is several rules rather than one threshold, and do you set thresholds from data. An answer that mentions only a score cutoff shows you have only touched the surface.
  2. Break refusal into three rules with different timing. Score too low: decidable before generation, saving a model call. Sources conflict: also decidable before generation, by finding differing numbers about the same thing across blocks. You then either present both with their update dates, or pick the newer one when an authoritative signal backs it, such as meeting notes that flagged the discrepancy. Which of the two is a product decision, but silently letting the model pick is never an option. Question outside coverage: only decidable after generation, when citation verification leaves you with zero verified citations.
  3. Stress that the three responses must read differently. 'Nothing relevant in the knowledge base, try rephrasing or check whether the document was ingested' is a different instruction to the user than 'we found related documents but none of them answers this'. Collapsing both into 'sorry, I don't know' throws away information.
  4. Then the cost half. Too high: answerable questions get blocked, the user is told nothing was found while the material is in fact indexed. That is the most trust-damaging failure and it is nearly invisible in logs. Too low: weak passages enter the context and the model answers from irrelevant material, which is worse because the answer still looks cited.
  5. How to set it: run a set of questions with known answers and known non-answers, look at where the two score distributions separate, and pick a point according to which error you fear more. Scores have no absolute scale, so the deliverable is the procedure, not the number.
  6. Expected follow-up: what if one score threshold is not enough? Add signals rather than tuning the number: the gap between top and second score, the number of hits above threshold, and the post-generation verification result are all steadier than the raw score.

  答题要点

  • 拒答不是一条线而是三条：分数过低、材料冲突（都在生成前判）、超出材料覆盖范围（只能生成后按引用校验结果判）。
  • 冲突检出后并列两说还是按更新日期择一，是产品决策；只有在有权威信号背书时择一才站得住，否则老实并列。
  • 三种情况的话术必须不同，因为它们给用户的下一步动作不同。
  • 定高了会把能答的问题挡住，用户看到查不到而材料其实在库里，最伤信任且日志里看不见。
  • 定低了会让噪声材料进上下文，错误更隐蔽，因为回答看起来仍然带着引用。
  • 定法是拿已知有答案与已知没答案的两组问题跑分数分布，按更怕哪种错取点；换语料或换检索方式都要重定。

  Key points

  • Refusal is three rules, not one: low score and source conflict decided before generation, out-of-coverage decided after generation from the verification result.
  • On conflict, presenting both versions versus picking the newer one is a product decision; picking only holds up when an authoritative signal backs it.
  • The three responses must be worded differently because each implies a different next action for the user.
  • Too high blocks answerable questions; the user is told nothing exists while it does, which is the most damaging and least visible failure.
  • Too low lets weak passages in, producing errors that are harder to spot because the answer still carries citations.
  • Set it by comparing score distributions over answerable and unanswerable question sets, then choose based on which error is worse; re-tune whenever the corpus or retriever changes.

D7 第一周综合：把六天的零件装成一个可一键启动的检索问答服务并复盘

• 你刚拼出来的这个检索问答系统，现在最大的风险在哪里？你打算怎么证明这个判断？What is the biggest risk in the RAG service you just assembled, and how would you prove that judgment?

  国内高频海外高频深入#evaluation#risk-assessment#retrospective

  分析过程 · 先想清楚再作答

  1. 这题有两半，后半句才是题眼。说出一个风险不难，难的是给出一个能证伪你自己判断的方法——答不出后半句，前半句就只是意见。
  2. 先排除两个常见的错误答案：说「幻觉」太笼统，没有指向任何可动的地方；说「延迟」则是把看得见的问题当成最大风险。
  3. 真正的最大风险是**没有评估**：切块大小、取几条、门槛定多少、两路怎么加权，全是拍出来的。它最重要的地方在于它让所有其他风险都无法验收——你连「改了之后变好还是变坏」都说不出口。
  4. 怎么证明：先从语料反向出一份带标准答案文档的问题集，刻意掺进无答案问题和需要跨文档的多跳问题；再实现召回率与排序指标，给当前配置跑出一个基线；然后把一个参数来回改两次，看指标动不动。如果指标对参数完全不敏感，说明是评估集有问题，不是系统没问题。
  5. 补一句成本口径：每一项优化都要同时报三笔账——指标涨了多少、延迟涨了多少、钱涨了多少。只报第一笔的结论不能用。
  6. 可预期的追问：评估集多大才够？先做二十题能覆盖主要问题类型的小集，用它挡住明显的退步；等真实用户问题攒起来，再按真实分布扩到几百题。一上来就追求规模，只会得到一堆自己出的、跟真实用法无关的题。

  How to reason about it · think before answering

  1. There are two halves here and the second is the real question. Naming a risk is easy; giving a method that could falsify your own claim is what separates answers from opinions.
  2. Rule out two common wrong answers: 'hallucination' is too vague to act on, and 'latency' mistakes a visible problem for the biggest one.
  3. The biggest risk is the absence of evaluation. Chunk size, top-k, thresholds and route weights were all guessed, and that makes every other risk unverifiable: you cannot even say whether a change helped.
  4. How to prove it: build a question set from the corpus with known answer documents, deliberately including unanswerable and multi-hop questions; implement recall and ranking metrics; produce a baseline for the current configuration; then move one parameter back and forth and watch whether the metrics move. If they do not move at all, the evaluation set is wrong, not the system.
  5. Add the accounting rule: every optimisation reports three numbers, metric gain, latency added and cost added. A claim with only the first is not usable.
  6. Expected follow-up: how large must the set be? Start with roughly twenty questions covering the main question types to catch obvious regressions, then grow toward the real distribution once you have actual user questions. Chasing size first only yields questions you invented yourself.

  答题要点

  • 最大的风险是没有评估：所有参数都是拍的，导致任何改动的好坏都无法判断。
  • 证明方式是先建标准答案集，刻意包含无答案问题与多跳问题，再跑出当前配置的基线。
  • 用参数扰动反过来验证评估集本身：指标对参数完全不敏感，说明题出得有问题。
  • 每项优化同时报三笔账：指标、延迟、成本；只报指标的结论不能用。
  • 评估集先小而全，覆盖问题类型即可，等真实问题攒起来再按真实分布扩大。

  Key points

  • The biggest risk is having no evaluation: every parameter was guessed, so no change can be judged.
  • Prove it by building a golden set with known answer documents, including unanswerable and multi-hop questions, then baseline the current configuration.
  • Validate the set itself by perturbing parameters: metrics that never move mean the questions are wrong.
  • Report three numbers per optimisation: metric gain, added latency, added cost.
  • Start small but well covered, then grow toward the real question distribution.

D8 评估先行：搭 golden set、算召回与排序指标、用模型当裁判判忠实度

• 用模型当裁判来评 RAG 的忠实度，有哪些系统性偏差？你怎么发现它们、又怎么证明你的裁判可信？What systematic biases does an LLM judge have when scoring RAG faithfulness, and how do you detect them and prove your judge is trustworthy?

  国内高频海外高频深入#llm-as-judge#evaluation#faithfulness

  分析过程 · 先想清楚再作答

  1. 这题的题眼在后半句。能背出「位置偏好、长度偏好、自我偏好」三个名词的人很多，能说出「怎么证明可信」的很少——面试官要的是后者。
  2. 先把三个偏差和各自的缓解手段一一对应：位置偏好用逐条独立打分代替两两比较，非要比较就交换顺序跑两遍、结论不一致判平局；长度偏好用逐句判定加比例计分，写得越长分母越大，长度红利自动消失；自我偏好用跨供应商或跨档位的模型评判，生成和评判不同源。
  3. 再补两条提示词层面的：给死评分锚点，1.0 / 0.6 / 0.3 各自是什么必须写明，否则同一份输入不同天给的分都不一样；强制结构化输出并要求把没支撑的句子原样列出，这是人工复核的抓手。
  4. 证明可信只有一条路：人工抽检算一致率。分层抽十到三十条——各类型都要有、命中和没命中都要有、裁判给高分和低分都要有，只判一个二元问题（有没有材料外的内容），跟裁判的结论比对。低于 0.8 就不能拿它的分数做拦合并这类决策。
  5. 一个能加分的细节：一致率很高不一定是好消息。如果抽的十条都是「答案原样抄自材料」的简单题，判对是理所当然的，这时候 100% 说明的是抽检没难度，不是裁判可靠。
  6. 可预期的追问是「裁判本身会不会坏」。答案是给裁判写探针：喂几组已知正确答案的输入（照抄材料的、明显编造的），每次跑评估都验一遍——评估系统坏掉的方式最阴险，分数照常输出，只是不再有意义。

  How to reason about it · think before answering

  1. The second half of the question is the discriminator. Plenty of people can name position, length, and self-preference bias; few can say how they prove the judge is trustworthy.
  2. Pair each bias with its mitigation: position bias - score pointwise instead of pairwise, and if you must compare, swap the order and call disagreement a tie; length bias - decompose into claims and score a ratio, so a longer answer grows its own denominator; self-preference - judge with a different vendor or tier than the generator.
  3. Add two prompt-level requirements: fixed rubric anchors (spell out what 1.0, 0.6 and 0.3 mean, or the same input scores differently on different days) and forced structured output that quotes the unsupported sentences verbatim, which is what makes human review possible.
  4. Proving trust has exactly one route: human spot-checks and an agreement rate. Stratify ten to thirty items across types, hits and misses, high and low judge scores; answer one binary question only - is anything here not in the material - and compare. Below 0.8 the judge's scores cannot gate a merge.
  5. A detail that scores points: a very high agreement rate may mean your spot-check was too easy. If all ten sampled answers copy the material verbatim, agreeing is trivial and 100% says nothing about the judge.
  6. Expected follow-up: can the judge itself break? Add probes - fixed inputs with known verdicts, one faithful and one obviously fabricated, checked on every run. An evaluation system fails silently: the numbers keep coming, they just stop meaning anything.

  答题要点

  • 三个偏差：位置偏好、偏爱长答案、自己评自己，各自有对应的缓解手段。
  • 逐条独立打分代替两两比较；逐句判定按比例计分抵消长度红利；生成与评判不同源。
  • 提示词要给死评分锚点，并强制结构化输出、列出没支撑的句子。
  • 可信度靠人工分层抽检算一致率，低于 0.8 不能用它做拦合并的决策。
  • 给裁判本身写探针，每次跑评估都验一遍它有没有坏。

  Key points

  • Three biases: position, verbosity, and self-preference, each with a matching mitigation.
  • Score pointwise rather than pairwise; decompose into claims and score a ratio to kill the length premium; never let the generator judge itself.
  • Pin rubric anchors in the prompt and force structured output that quotes unsupported sentences.
  • Establish trust through stratified human spot-checks and an agreement rate; below 0.8 the judge cannot gate merges.
  • Add probes with known verdicts so a broken judge is caught on every run.

D9 混合检索与重排：两路召回、倒数排名融合，再用交叉编码器把前几名重新排一遍

• 你把纯向量检索换成了混合检索加重排，上线之后评估指标反而掉了。你会怎么排查？You replaced pure vector retrieval with hybrid search plus reranking, and after shipping it your eval metrics went down. How do you investigate?

  国内高频海外高频深入#hybrid-search#evaluation

  分析过程 · 先想清楚再作答

  1. 这题考的是你有没有真的做过分阶段归因。答「调一下权重再看看」就输了——那是在猜，不是在查。
  2. 第一步是拆档跑，不是改代码：纯关键词、纯向量、混合、混合加重排四档在**同一份评估集、同一个上下文预算**下各跑一遍。指标掉在哪一档就在哪一档找原因，这一步能立刻区分「融合坏了」和「重排坏了」。
  3. 第二步问一个具体问题：掉的是召回率还是排序指标？召回率掉说明答案根本没进上下文，是候选池或者预算的问题；排序指标掉而召回率没动，说明答案还在、只是被挤到了后面，那是融合权重或重排模型的问题。这两类故障的解法完全不同。
  4. 第三个常见根因是召回深度。每路取多少条这个旋钮方向反直觉：取深了不是更保险，是把噪声也一起投了票。我在一份 134 块的语料上实测过，每路从取 50 收到取 5，混合那一档的召回率从 87.5% 回到 93.8%、多跳档从 50% 回到 75%，而 nDCG 反而掉了近 0.1——两个指标会打架，先想清楚业务要哪个。
  5. 第四个根因是评估口径被悄悄改了。上下文预算、命中判定、候选池深度只要动过一个，新旧数字就不可比，这时候「掉了」可能根本不是真的掉了。
  6. 可预期的追问：怎么防止下次再踩？答把四档对照做成一条命令、把上一版报告存成基线、指标退步就以非 0 退出码拦住合并——这就是评估要先于优化的原因。

  How to reason about it · think before answering

  1. This question tests whether you have actually done stage-by-stage attribution. Answering `I would tune the weights and see` loses — that is guessing, not investigating.
  2. Step one is to run the stages apart, not to change code: pure keyword, pure vector, hybrid, and hybrid plus rerank, all on the **same eval set with the same context budget**. Whichever stage the drop appears in is where you look, and this alone separates `fusion is broken` from `reranking is broken`.
  3. Step two asks a specific question: did recall drop, or did the ranking metrics drop? A recall drop means the answer never entered the context at all — a candidate-pool or budget problem. Ranking metrics dropping while recall holds means the answer is still there but pushed down — a fusion-weight or rerank-model problem. The two failures have completely different fixes.
  4. A third common root cause is recall depth. This knob runs against intuition: going deeper is not safer, it lets noise vote too. On a 134-chunk corpus I measured that narrowing each route from 50 to 5 took hybrid recall from 87.5% back to 93.8% and multi-hop from 50% to 75%, while nDCG fell by almost 0.1. The metrics fight each other, so decide which one the product needs first.
  5. A fourth root cause is that the eval protocol quietly changed. Touch the context budget, the hit rule, or the candidate depth, and the old and new numbers stop being comparable — in which case the `drop` may not be a drop at all.
  6. Expected follow-up: how do you avoid this next time? Make the four-way comparison a single command, store the previous report as a baseline, and fail the build with a non-zero exit code on regression. That is precisely why evaluation comes before optimization.

  答题要点

  • 先拆档跑四种配置，在同一份评估集和同一个上下文预算下归因，不要一上来就调参。
  • 区分召回率掉与排序指标掉：前者是候选池或预算问题，后者是融合或重排问题。
  • 查召回深度：每路取太深会把噪声也投进融合，收窄反而可能救回召回率。
  • 确认评估口径没被改：预算、命中判定、候选池深度动过一个，新旧数字就不可比。
  • 把四档对照固化成一条命令加一份基线报告，指标退步直接拦住合并。

  Key points

  • Run all four configurations separately for attribution, on one eval set with one context budget, before touching any parameter.
  • Separate a recall drop from a ranking drop: the first is a candidate-pool or budget issue, the second is a fusion or rerank issue.
  • Check recall depth: taking too many per route lets noise vote, and narrowing it can bring recall back.
  • Confirm the eval protocol did not change; touching budget, hit rule, or candidate depth makes old and new numbers incomparable.
  • Freeze the four-way comparison into one command plus a baseline report, and block merges on regression.