面试题库

14 天 RAG：从检索到可信回答

D11 高级索引：父子文档、摘要索引、上下文检索，以及树状聚合与图检索的取舍

• 父子索引和上下文检索都在补『块被切碎』这个问题，它们的差别到底在哪？Parent-child indexing and contextual retrieval both patch the same problem — chunks losing their context. What actually distinguishes them?

  国内高频海外高频进阶#indexing#contextual-retrieval#chunking

  分析过程 · 先想清楚再作答

  1. 这题的题眼是『补的是哪一半』。答成『一个是切块技巧、一个是加提示词』就是在描述实现，面试官想听的是它们各自作用在检索管道的哪一段。
  2. 拆的办法是把管道分成两段问：检索时看到什么、生成时看到什么。父子索引改的是**生成侧**——检索单位还是小块，只是命中之后把上下文单位换成大块；上下文检索改的是**检索侧**——块头拼进去是为了让这一块能被检索到，模型生成时并不需要它。
  3. 结论：父子索引解决『找到了但看不全』，上下文检索解决『看得全但找不到』。前者不改变谁被检索到，后者不改变模型看到多少。它们正交，可以叠加。
  4. 这个差别还决定了它们各自要用什么指标去量：上下文检索动的是名次，用召回率和 nDCG 量得到；父子索引动的是『材料够不够答』，召回率这种二值指标量不出来。我们那份 20 题评估集单文档档已经 100% 饱和，父子索引在表里跟基线持平——那不是它没用，是尺子量不了它。
  5. 顺着这条差异能推出一个立刻能用的优化：既然块头只服务检索，就不该进上下文。它进了上下文就是在每一次查询里白占预算，而且这笔钱是长期的。我们的实验里把这个开关一改，五列指标一个不变，600 token 的预算里多装进了 36 个 token。
  6. 代价也不同：父子索引的代价是索引条目变多、每次装进上下文的东西变大；上下文检索的代价是一次性要给每块调一次模型，加上索引 token 永久变大。前者是空间，后者是时间加空间。
  7. 可预期的追问是『那我全都上』。答案是先看失败案例：日志里是『材料不完整』多，还是『压根没检索到』多。没有对应的失败模式就不该上，这两个手法都不是免费的。

  How to reason about it · think before answering

  1. The hinge is which half of the pipeline each one fixes. Answering 'one is a chunking trick, the other adds a prompt' just describes implementations; the interviewer wants to know where each acts.
  2. Split the pipeline in two and ask separately: what does the retriever see, and what does the generator see. Parent-child changes the generation side — retrieval still runs on small chunks, but a hit is swapped for its parent. Contextual retrieval changes the retrieval side — the header exists so the chunk can be found at all, and the generator does not need it.
  3. Conclusion: parent-child fixes 'found it but can't read it'; contextual retrieval fixes 'readable but never found'. Neither changes what the other changes, so they compose.
  4. That difference also dictates which metric can see each one. Contextual retrieval moves rank, so recall and nDCG catch it. Parent-child moves 'is the evidence sufficient to answer', which a binary recall metric cannot see. Our 20-question set is already saturated at 100% on single-document questions, so parent-child comes out level with the baseline — that is the ruler failing, not the technique.
  5. That difference yields a free optimization: since the header only serves retrieval, keep it out of the context window. Leaving it in pays rent on every single query. Flipping that one switch in our lab freed 36 tokens inside a 600-token budget with every metric unchanged.
  6. The costs differ too. Parent-child costs index entries and a bigger context unit. Contextual retrieval costs one model call per chunk up front plus a permanently larger index. One is space; the other is time and space.
  7. Expect the follow-up 'why not both'. Look at the failure logs first: are you mostly seeing incomplete evidence, or nothing retrieved at all? Without the matching failure mode, neither is worth its price.

  答题要点

  • 父子索引作用在生成侧：检索单位是小块，上下文单位换成父块，解决『找到了但看不全』。
  • 上下文检索作用在检索侧：块头让块能被检索到，解决『看得全但找不到』。
  • 两者正交可叠加；块头只该进索引不该进上下文，否则每次查询都在为它付钱。
  • 父子索引的代价是索引条目与上下文单位变大；上下文检索的代价是一次性建索引调用加永久变大的索引。
  • 选哪个看失败案例：材料不完整选前者，压根没检索到选后者。

  Key points

  • Parent-child acts on the generation side: retrieve small, swap in the parent for context. It fixes 'found but unreadable'.
  • Contextual retrieval acts on the retrieval side: the header makes the chunk findable. It fixes 'readable but never found'.
  • They are orthogonal and compose; keep the header in the index only, never in the context window.
  • Parent-child costs more index entries and a larger context unit; contextual retrieval costs one call per chunk plus a permanently larger index.
  • Pick based on the observed failure: incomplete evidence points to the former, zero retrieval to the latter.

D14 综合项目与复盘：多租户企业知识库问答，一张 RAG 决策地图与面试专题

• 你做过的这个 RAG 项目里，哪个决定你现在会改？为什么？Looking back at the RAG project you built, which decision would you change now, and why?

  国内高频海外高频深入#retrospective#evidence#chunking

  分析过程 · 先想清楚再作答

  1. 这题看着是软性问题，其实区分度极高。答「暂时没有」等于承认没做过复盘；答成一长串自我批评又会显得没有判断力。面试官真正在听的是：你能不能把一个决定、它当时的依据、后来的证据、以及新的判断，四样东西串成一条链子说清楚。
  2. 怎么拆：挑一个**当时有理由、后来被数据推翻**的决定，而不是一个「当时就知道是凑合」的决定。前者证明你有量化的习惯，后者只证明你赶过工期。所以答案的骨架固定是四段——当时选了什么、依据是什么、后来量到了什么、现在的判断是什么。
  3. 本课里有一个现成的样本：某一天先量到「给每个块拼上标题块头之后，命中率不变、索引 token 涨一成、答案文档平均名次从 2.88 退到 3.25」，据此写下「块头对关键词检索是负收益」。后来换成按文档结构切块再复核，这条名次退化**没有复现**。原因是切法变了：固定长度硬切时块边界跟小节边界不对齐，块头会把不属于这一块的标题词塞进来；按结构切时块本身就落在一个小节里，块头补的信息跟块里已有的高度重合。所以正确的表述不是「块头有害」，而是「**块头在块边界与结构不对齐时才有害**」。
  4. 这条链子的价值在于它演示了一个可复用的动作：**给每个结论标出它绑定的前提**。前提变了就要重跑，不能拿新配置去配旧结论。顺着这个思路还能给出第二个例子：曾经写过「向量侧确实是正收益」，后来把融合前的两路拆开数了一遍才发现，向量路独有的候选只占很小一部分，其中含答案文档的次数是零——那个「涨」根本不是语义检索带来的，于是这条结论被自己推翻。
  5. 可预期的追问：那你以后怎么避免这类错误？答两条具体的：一是每个数字旁边写清它绑定了哪几个前提（语料、题集、预算、切法），二是报结论前先问自己一句「这是这次实验测出来的，还是这个结构必然导致的」——前者要标边界，后者才能直接讲。

  How to reason about it · think before answering

  1. This looks like a soft question but it separates people sharply. Saying 'nothing yet' admits you never ran a retrospective; a long list of self-criticism reads as poor judgement. What the interviewer is listening for is whether you can chain four things together: the decision, the evidence you had then, the evidence you got later, and your current call.
  2. How to pick: choose a decision that was justified at the time and later overturned by data, not one you always knew was a shortcut. The first proves you measure; the second only proves you were behind schedule. So the answer has a fixed four-part shape — what you chose, on what basis, what you measured later, and what you now believe.
  3. This course supplies a ready example. One day measured that prepending a heading-path header to every chunk left hit rate unchanged, grew index tokens by about ten percent, and pushed the answer document's mean rank from 2.88 to 3.25 — hence 'headers hurt keyword retrieval'. A later day re-ran the same comparison under structure-aware chunking and the rank regression did not reproduce. The reason was the chunker: with fixed-length cuts, chunk boundaries do not line up with section boundaries, so the header injects heading terms that do not belong to that chunk; with structure-aware cuts, each chunk already sits inside one section and the header largely restates what is already there. The correct statement is therefore not 'headers hurt' but 'headers hurt when chunk boundaries are misaligned with document structure'.
  4. The value of the chain is that it demonstrates a reusable habit: attach the premises to every conclusion. Change a premise and you owe a re-run; you may not pair new settings with an old conclusion. The same reasoning yields a second example: an earlier claim that 'the vector route is clearly a net gain' collapsed once the two routes were counted separately before fusion — the vector-only candidates were a small share and contained the answer document zero times, so the improvement was never semantic at all.
  5. Expected follow-up: how will you avoid this class of error in future? Give two concrete practices. Write the bound premises next to every number — corpus, question set, budget, chunker. And before publishing any conclusion, ask whether it was measured by this experiment or forced by the structure of the implementation; the first needs its boundaries stated, only the second can be asserted flatly.

  答题要点

  • 答案要串成四段：当时选了什么、依据是什么、后来量到了什么、现在的判断是什么。
  • 挑一个当时有理由、后来被数据推翻的决定，而不是一个当时就知道在凑合的决定。
  • 样本：块头从「对关键词检索有害」修正成「块边界与结构不对齐时才有害」，因为切法这个前提变了。
  • 每个数字旁边写清它绑定的前提；前提变了就必须重跑，不能新配置配旧结论。
  • 报结论前先分类：这是实验测出来的，还是实现结构必然导致的——前者要标边界。

  Key points

  • Structure the answer in four beats: the choice, the evidence then, the evidence later, the call now.
  • Pick a decision that was defensible at the time and later overturned by data, not one you knew was a shortcut.
  • Worked example: 'headers hurt keyword retrieval' was corrected to 'headers hurt when chunk boundaries misalign with structure', because the chunker premise changed.
  • Record the premises bound to every number; when a premise changes you owe a re-run rather than a reinterpretation.
  • Classify before asserting: measured by this experiment, or forced by the implementation's structure — the former needs its boundaries stated.