面试题库

14 天 RAG：从检索到可信回答

D1 为什么要检索：幻觉、知识截止与长上下文的代价，以及一个纯关键词的最小 RAG

• 一个检索增强生成系统答错了，你怎么定位是检索的锅还是生成的锅？A retrieval-augmented generation system gave a wrong answer. How do you determine whether retrieval or generation is at fault?

  国内高频海外高频进阶#debugging#failure-modes#evaluation

  分析过程 · 先想清楚再作答

  1. 题眼在「怎么定位」，不在「有哪些原因」。答成一串可能原因的罗列就输了，面试官想听的是一个有先后顺序、能落到具体动作的排查流程。
  2. 先给最省时间的第一步：把这次检索出来的几段原文原样打印出来，自己读一遍。正确答案不在里面就是检索的锅，在里面而模型没用上才是生成的锅。这一步三十秒，能省掉大半天的瞎猜。
  3. 然后把链路展开成五个环节——切块、建索引、检索、组装上下文、生成——并给出「排查从右往左、修复从左往右」这条口径：从右往左是因为你最先看到的是生成结果，从左往右是因为左边的错会被右边放大。
  4. 补充几个能把环节钉死的症状：答案「半对」多半是切块把一条完整规则切断了；检索结果里混着一眼不相干的东西多半是解析没做干净；模型无视材料用先验知识作答，通常是提示词里少了「只能依据资料回答」；引用编号和内容对不上，那是生成侧漏读或串了行。
  5. 最后落到工程做法：这套排查要能重复做，就必须把每次请求的检索结果、进上下文的段落、最终回答一起记下来，否则线上出问题时你根本复现不了。到了要批量做的时候，就得换成一批固定问题加指标，而不是一条条人工看。
  6. 可预期的追问：如果检索确实没捞到，改提示词有没有用？答案是没用——材料里没有的东西，再好的指令也只能换一种编法。这句话最能证明你分清了两层。

  How to reason about it · think before answering

  1. The question asks how you localise the fault, not what the possible causes are. Listing causes loses; the interviewer wants an ordered procedure that ends in concrete actions.
  2. Give the cheapest first step: print the retrieved passages verbatim and read them. If the correct answer is not in there, retrieval is at fault. If it is in there and the model ignored it, generation is at fault. Thirty seconds, and it removes most of the guesswork.
  3. Then lay out the five stages — chunking, indexing, retrieval, context assembly, generation — with the rule: diagnose right to left, fix left to right. You see the generated answer first, but an error on the left is amplified by everything to its right.
  4. Add symptoms that pin down a stage: half-correct answers usually mean a rule was split across chunks; obviously irrelevant hits usually mean dirty parsing; the model ignoring the supplied material usually means the prompt never said it must; citation numbers that do not match their content point at generation.
  5. Land it in engineering terms: to run this procedure repeatedly you must log the retrieved hits, the passages that entered the context, and the final answer together, otherwise production issues are unreproducible. At scale this becomes a fixed question set with metrics rather than case-by-case reading.
  6. Expected follow-up: if retrieval missed the document, will prompt tuning help? No. Nothing in the prompt can conjure material that was never supplied.

  答题要点

  • 第一步永远是把检索出来的原文打印出来读一遍，判断正确答案在不在里面。
  • 把链路拆成切块、建索引、检索、组装上下文、生成五个环节，排查从右往左、修复从左往右。
  • 用症状钉环节：半对多半是切块问题，混入无关结果多半是解析问题，无视材料多半是提示词缺约束，引用与内容对不上是生成问题。
  • 检索没捞到时改提示词没有意义，材料里没有的东西模型只能编。
  • 要能重复排查就必须把检索结果、进上下文的段落和最终回答一起记录下来。

  Key points

  • Always start by printing the retrieved passages and checking whether the correct answer is present at all.
  • Split the pipeline into chunking, indexing, retrieval, context assembly and generation; diagnose right to left, fix left to right.
  • Use symptoms to pin the stage: half-correct answers point at chunking, irrelevant hits at parsing, ignored material at the prompt, mismatched citations at generation.
  • If retrieval missed the document, prompt changes cannot help; the material simply is not there.
  • Log retrieved hits, the passages that entered the context, and the final answer together, or production failures are unreproducible.