面试题库

14 天 RAG：从检索到可信回答

D3 文档进来这一关：PDF 与 HTML 解析、表格与扫描件、清洗规则和必须留下的元数据

• 为什么说解析质量决定了检索质量的上限？举一个具体的传导链条。Why is parsing quality the ceiling on retrieval quality? Walk through one concrete chain of propagation.

  国内高频海外高频基础#ingestion#data-quality#failure-analysis

  分析过程 · 先想清楚再作答

  1. 这是一道送分题，但很多人答成口号。判据只有一个：有没有给出一条能落到具体现象上的链条，而不是重复一遍「垃圾进垃圾出」。
  2. 先说清位置：解析在切块、建索引、检索、组装、生成这五环之前，是第零环。它的错误会被后面每一环放大，而且后面每一环都无法察觉——它们只是在忠实地处理一段已经错了的文字。
  3. 给一条具体链条：一张套餐配额表在 PDF 里丢了一列分隔符，抽出来串了行；切块照着错误的边界切，「专业版」和隔壁那一栏的值被切进同一块；索引把错误的词对记进倒排表；用户问「专业版存储配额多少」，这一块分数很高被排到第一；模型只依据给定材料回答，于是给出一个错误但带着正确引用编号的答案。
  4. 点破最要命的一句：这条链上没有任何一环会报错，回答甚至是带出处的，看起来比平时更可信。所以解析的错误不能靠事后发现，只能靠入口处的断言拦。
  5. 反过来说明「上限」二字：后面所有优化——向量、混合检索、重排、查询改写——优化的都是「从候选里挑得更准」。材料本身错了，挑得再准也是错的，所以它们的天花板由解析封死。
  6. 可预期的追问：那怎么证明是解析的锅？答案接回 D1 那条习惯——排查从右往左看，把检索出来的原文打印出来自己读一遍，如果原文本身就是串行的，那就不用再往生成侧查了。

  How to reason about it · think before answering

  1. This is a giveaway question that many people answer with a slogan. The only test is whether you produce a chain that lands on a concrete symptom instead of repeating garbage in, garbage out.
  2. Place it first: parsing sits before chunking, indexing, retrieval, context assembly and generation. Its errors are amplified by every later stage, and none of those stages can detect the problem because each is faithfully processing text that is already wrong.
  3. Give the chain: a pricing table in a PDF loses one column separator and comes out with cells shifted. Chunking splits on those wrong boundaries, so a plan name ends up next to the neighbouring column value. The index records the wrong term pairing. A user asks about that plan's storage quota, the corrupted chunk scores highest, and the model, faithfully answering only from the provided material, returns a wrong answer carrying a correct-looking citation.
  4. Name the nastiest part: nothing on that chain raises an error, and the answer even comes with a source, so it looks more trustworthy than usual. Parsing errors cannot be caught after the fact, only by assertions at ingest.
  5. Explain the word ceiling: every later optimisation, dense retrieval, hybrid search, reranking, query rewriting, improves how well you pick from the candidates. If the material itself is wrong, picking better still returns something wrong, so parsing caps all of them.
  6. Expected follow-up: how do you prove parsing is at fault? Reuse the habit from day one. Diagnose right to left and print the retrieved passages verbatim. If the source text is already scrambled, there is no point looking at the generation side.

  答题要点

  • 解析是五个环节之前的第零环，它的错误会被后面每一环放大，而后面每一环都察觉不到。
  • 具体链条：表格串行 → 切块按错误边界切 → 倒排表记进错误词对 → 检索把它排第一 → 模型据此给出带引用的错误答案。
  • 最危险的是全程零报错，且答案带着出处，看起来比平时更可信。
  • 后面所有优化解决的是「挑得更准」，材料本身错了就都无效，所以上限由解析封死。
  • 定位方法是排查从右往左：先把检索到的原文打印出来读一遍，原文错了就不必再查生成侧。

  Key points

  • Parsing is stage zero, before the five-stage pipeline; its errors are amplified downstream and invisible to every later stage.
  • Concrete chain: a shifted table, chunking on wrong boundaries, wrong term pairs in the index, that chunk ranked first, and a wrong answer delivered with a citation.
  • The dangerous part is that nothing errors out and the answer carries a source, so it looks more credible than usual.
  • Later techniques only improve selection from candidates; if the material is wrong, better selection still returns something wrong.
  • Diagnose right to left: print the retrieved passages first, and if the source text is already broken, stop looking at the generation side.