Interview Bank

RAG in 14 Days: From Retrieval to Trustworthy Answers

D4 Chunking Strategies: Five Approaches — Fixed, Recursive, Structure-Based, Parent-Child, and Semantic — and Choosing by Evaluation, Not Intuition

• How do you decide on chunk size? Name two metrics you would look at, and one counterexample.你怎么决定切块大小？说出你会看的两个指标和一个反例。

  Common in ChinaCommon overseasIntermediate#chunking#evaluation

  How to reason about it · think before answering

  1. The question is about method, not about a number. Answering with a specific default (512 tokens, 1000 characters) already loses it — the interviewer wants to hear that you have a procedure.
  2. State the tension first: large chunks dilute the signal and cost context; small chunks lose the surrounding meaning so the model cannot use them. The two metrics you name should map onto those two failure modes.
  3. Metric one is retrieval-side hit rate: did a document that actually answers the question make it into the context. Metric two is generation-side usability, cheaply proxied by the fraction of chunks that end mid-sentence, and more seriously by faithfulness and whether citations resolve.
  4. Add the point that separates candidates: both metrics must be compared under the same token budget, never under a fixed top-k. With fixed k, bigger chunks simply buy more text and win for the wrong reason.
  5. Make the counterexample concrete: raising chunk size from 400 to 1200 characters can lift hit rate purely because whole short documents now fit in one chunk, which means retrieval stopped doing anything and you are back to stuffing full documents. The metric improved while the system got worse.
  6. Expect the follow-up: where do you start on day one. Pick the strategy from the document type first (structural splitting whenever headings exist), start around 300 to 500 characters with 10 to 20 percent overlap, then build a golden set immediately and iterate. A starting point is not a conclusion.

  分析过程 · 先想清楚再作答

  1. 这题的题眼是「怎么决定」，不是「多大合适」。答一个具体数字（512 token、1000 字符）就已经输了——面试官想看的是你有没有一套定法，而不是你记得住哪个默认值。
  2. 先把矛盾摆出来：块大则信噪比低、上下文贵，块小则单块缺语境、模型答不出所以然。切块大小就是在这两头之间找位置，所以两个指标必须分别对应这两头。
  3. 第一个指标是检索侧的命中率——答案文档有没有进上下文。第二个是生成侧的可用性，最省事的代理指标是切碎率，也就是有多少块结尾停在半句话上；再往前一步就是忠实度和引用是否可定位。
  4. 关键补一句：两个指标必须在**同一个 token 预算**下比，不能按「取前 k 块」比。k 固定时块越大塞进去的字越多，大块切法会赢在买得多而不是切得准上。这一句往往是这道题的区分点。
  5. 反例要具体。最好用的一个是：把块从 400 字调到 1200 字，命中率不降反升——但那是因为一整篇短文档被当成一块塞了进去，检索其实什么都没做，等于退化成了全文投喂。指标涨了，系统更差了。
  6. 可预期的追问是「那你第一次上手时从哪个数字起步」。答：先按文档类型选切法（有标题层级就按结构切），块长从 300 到 500 字起步、重叠取一到两成，然后立刻建一组标准问题跑评估，用两三轮迭代把它调到位。起步值是起步值，不是结论。

  Key points

  • Choose the strategy from the document type first, then tune length: split on headings whenever the structure survives parsing.
  • Watch two metrics: retrieval hit rate on one side, mid-sentence break rate (then faithfulness and citation resolvability) on the other.
  • Compare under an equal token budget, never a fixed top-k, or larger chunks win by buying more text.
  • Counterexample: hit rate rises after enlarging chunks because whole documents now fit in one chunk and retrieval has effectively stopped working.
  • Start near 300 to 500 characters with 10 to 20 percent overlap, then iterate against a fixed question set instead of guessing.

  答题要点

  • 先按文档类型选切法，再调长度：有标题层级就按结构切，没有结构才谈固定长度或语义。
  • 看两个指标：检索侧的命中率，生成侧的切碎率（进一步是忠实度与引用可定位性）。
  • 两个指标必须在同一个 token 预算下比，不能按「取前 k 块」比，否则大块只是买得更多。
  • 反例：块调大后命中率上升，但那是因为整篇被当成一块，检索退化成全文投喂。
  • 起步值 300 到 500 字、重叠一到两成，然后靠一组固定问题迭代，不靠直觉定稿。
• What does parent-child chunking buy you, and when does it slow the system down instead?父子切块的收益是什么？它在什么情况下反而会拖慢系统？

  Common in ChinaCommon overseasIntermediate#chunking#parent-child

  How to reason about it · think before answering

  1. This question checks whether you know that the retrieval unit and the context unit can be two different things. Without that sentence, everything else is recitation.
  2. State the benefit compactly: small chunks go into the index so they are easy to match, and once a child is hit you follow the parent pointer and hand the model the whole section. You stop trading precision against completeness.
  3. Derive the slowdown from the costs. First, the context budget: every new child may drag in an entire parent, so an equal budget holds fewer distinct pieces and result diversity drops.
  4. Second, the write path: two levels to maintain, both recomputed on every document update, and chunk ids become harder to keep stable, which makes incremental sync noticeably more complex.
  5. Third, the condition under which the benefit disappears: when sections are already short, the parent and the child are nearly the same text, so you paid for two indexes and bought nothing. Parent-child suits long sections and deep hierarchies, not already fine-grained knowledge bases.
  6. Expect the follow-up: how is this different from simply using bigger chunks. Bigger chunks put the noise into the index; parent-child puts the noise only into the context. What gets matched stays short and clean.

  分析过程 · 先想清楚再作答

  1. 这题考的是你有没有意识到「检索单位」和「上下文单位」可以是两个东西。答不出这句话，后面说什么都是复述。
  2. 收益一句话说清：小块进索引，信噪比高、容易被找到；命中之后顺着父指针把整节回填给模型，语境完整。精度和完整度这次不用二选一。
  3. 拖慢的场景要从代价一条条推。第一条是上下文预算：每命中一个新子块可能拖进来一整个父节，同样的 token 预算装不下几条，检索结果的多样性反而变差。
  4. 第二条是写入侧：父子两套都要维护，文档更新时两边都要重算，块 id 的稳定性也更难保证，增量同步的复杂度明显上升。
  5. 第三条是收益消失的条件：当文档本身的小节就不长时，父块和子块差不多大，你付了两套索引的钱，什么也没多买到。所以父子切块适合长节、深层级的文档，不适合结构本来就细碎的知识库。
  6. 可预期的追问是「那和直接把块切大有什么区别」。答：切大是把噪声一起放进索引，父子是只把噪声放进上下文、不放进索引——被检索的那一段始终是干净的短文本，这是本质区别。

  Key points

  • The core idea is decoupling the retrieval unit from the context unit: small chunks get found, large chunks get understood.
  • The payoff is precision and completeness at the same time instead of trading one for the other.
  • Cost one: a single hit can drag in a whole parent, so an equal context budget holds fewer distinct results and diversity suffers.
  • Cost two: two index levels to maintain and recompute, which makes incremental sync on document updates considerably harder.
  • It stops paying off when sections are already short, because parent and child are nearly identical and you bought nothing for the extra cost.

  答题要点

  • 核心是把检索单位和上下文单位拆开：小块负责被找到，大块负责被读懂。
  • 收益是精度与完整度同时拿到，不用在信噪比和语境之间二选一。
  • 代价一：一次命中可能拖进整个父节，同样的上下文预算装得下的条数变少，结果多样性下降。
  • 代价二：父子两套索引都要维护与重算，文档更新时增量同步的复杂度明显上升。
  • 失效场景：文档小节本来就短时父子块差不多大，多付一套成本却没多买到东西。
• What overlap ratio would you use, and what concretely goes wrong when the overlap is too large?重叠区设成块长的百分之多少合适？重叠过大会带来什么具体问题？

  Common in ChinaCommon overseasBasic#chunking#overlap

  How to reason about it · think before answering

  1. This is a giveaway question, but the marks are in the second half, not the percentage. Stopping at 'usually ten to twenty percent' reads like someone who has never run it.
  2. Say what overlap is patching: fixed-length splitting cuts sentences in half, and overlap guarantees the broken sentence survives intact in at least one of the two neighbours. It is a patch for careless splitting, not an optimisation of its own.
  3. That yields the first conclusion: with structural or recursive splitting the boundaries already land on semantic positions, so the need for overlap drops sharply and can legitimately be zero. The ratio question is meaningless without naming the strategy.
  4. Give three concrete costs. Storage and tokens: at 400-character chunks, moving overlap from 0 to 80 grows total index tokens by roughly fifteen percent, which is storage cost in the vector store and comparison work at query time.
  5. Retrieval redundancy: the more neighbours overlap, the more likely the top results are three versions of the same passage. You think you handed the model three pieces of evidence; you handed it one, three times. Nothing fixes this before reranking.
  6. Citation resolution: when a sentence lives in two chunks, which one does the model cite. Expect the follow-up on deduplication: merge at the result layer using a content fingerprint or longest common substring, not by tweaking the chunker.

  分析过程 · 先想清楚再作答

  1. 这是一道送分题，但送分点不在那个百分比上，而在后半句。只答「一般一到两成」就停住的人，面试官会认为他没跑过。
  2. 先说清重叠在补救什么：固定长度切法会把句子从中间切开，重叠让被切开的那句话至少在相邻两块之一里是完整的。它是给「乱切」打的补丁，不是一个独立的优化。
  3. 由此推出第一个结论：如果你用的是按结构切或递归切，边界本来就落在语义位置上，重叠的必要性会大幅下降，甚至可以是零。**重叠比例这个问题的前提是切法**，脱开切法谈比例就是背数字。
  4. 过大的代价要说三笔，越具体越好。存储与 token：块长 400、重叠从 0 加到 80，索引 token 会涨一成半左右，这笔钱在向量库是存储费、在检索时是比对量。
  5. 检索冗余：相邻块越像，前几名越可能是同一段话的三个版本，你以为给了模型三条证据，其实是一条说了三遍。这一条在重排之前基本无解。
  6. 引用定位：同一句话出现在两个块里，模型标出处该标哪一个，这会直接变成引用校验环节要处理的边界情况。可预期的追问就是「那你怎么去重」，答按内容指纹或最长公共子串在结果层合并，而不是在切块层想办法。

  Key points

  • Ten to twenty percent of chunk length is the working range, but that number assumes fixed-length splitting.
  • With structural or recursive splitting the boundaries are already semantic, so overlap can be small or zero.
  • Cost one: index tokens and storage grow noticeably; at 400-character chunks, an 80-character overlap adds roughly fifteen percent.
  • Cost two: neighbouring chunks become near-duplicates, so the top results are several versions of one passage and the evidence diversity is illusory.
  • Cost three: a sentence spanning two chunks complicates citation attribution and forces result-level deduplication.

  答题要点

  • 经验区间是块长的一到两成，但这个数字的前提是你用的是固定长度切法。
  • 按结构或递归切时边界本来就在语义位置上，重叠可以很小甚至为零。
  • 过大代价一：索引 token 与存储明显上涨，块长 400 时重叠加到 80 大约涨一成半。
  • 过大代价二：相邻块高度相似，检索前几名变成同一段话的多个版本，证据多样性是假的。
  • 过大代价三：同一句话跨块出现，引用标注和去重都要额外处理。
• Semantic chunking costs considerably more than recursive splitting. How would you prove to your team that the money is well spent?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？

  Common in ChinaCommon overseasDeep dive#chunking#evaluation#cost

  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.

  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。

  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.

  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。

D11 Advanced Indexing: Parent-Child Documents, Summary Indexes, Contextual Retrieval, and the Trade-Offs of Tree Aggregation vs. Graph Retrieval

• Parent-child indexing and contextual retrieval both patch the same problem — chunks losing their context. What actually distinguishes them?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？

  Common in ChinaCommon overseasIntermediate#indexing#contextual-retrieval#chunking

  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.

  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。

  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.

  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。

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

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

  Common in ChinaCommon overseasDeep dive#retrospective#evidence#chunking

  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.

  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。

  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.

  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。