面试题库

14 天 RAG：从检索到可信回答

D2 embedding 与向量检索：相似度、维度与模型选型，把文本存进 pgvector

• 把 embedding 维度从 1536 降到 512，你会损失什么？什么场景下这个损失可以接受？What do you lose when you cut embedding dimensions from 1536 to 512, and when is that loss acceptable?

  国内高频海外高频进阶#embeddings#dimensions#cost

  分析过程 · 先想清楚再作答

  1. 这题考的是你会不会算账。只说「维度越低越省、精度越低」的答案没有区分度，面试官在等一个具体的成本模型和一个决策顺序。
  2. 先把三笔账列出来：存储与内存（向量数量乘维度乘每维字节数，近似最近邻索引要把它放进内存，所以基本等于机器预算）、检索延迟（每次比较就是一轮乘加，维度大致线性影响耗时）、检索质量（收益递减，低维段每加一档提升明显，高维段加倍只换来很小的改善）。
  3. 再说清降维为什么可行：主流模型用套娃式表示训练，重要信息压在靠前的维度上，所以直接截短再归一化仍然可用，这不是另训了一个小模型。截短必然有损失，损失多少只能在自己的数据上跑评估才知道。
  4. 给出决策顺序：先按存储与内存预算倒推一个维度上限，再从上限往下试两三档，看指标掉多少，掉得能接受就用低的。反过来「先选最高维再想办法省钱」基本都会返工。
  5. 点出可接受的典型场景：库很大而单条价值不高（比如日志、工单）、召回之后还有重排兜底（重排能把粗排的损失补回来一部分）、或者对延迟极敏感的在线场景。反过来法务、医疗这类一条都不能漏的场景就要谨慎。
  6. 可预期的追问：能不能不同文档用不同维度？不能——同一个索引里所有向量必须同维，改维度等于全库重建，这跟换模型是同一类迁移成本。

  How to reason about it · think before answering

  1. This is a cost-modelling question. 'Lower dimensions are cheaper but less accurate' earns nothing; the interviewer wants a cost model and a decision order.
  2. Lay out three costs: storage and memory (vector count times dimensions times bytes per dimension, which an ANN index must hold in RAM), query latency (roughly linear in dimensions), and retrieval quality, whose returns diminish sharply at the high end.
  3. Explain why truncation works at all: models trained with Matryoshka representations pack the most important information into the leading dimensions, so truncating and re-normalising keeps the vector usable. It is still lossy, and how lossy is an empirical question on your own data.
  4. Give the decision order: derive a dimension ceiling from your memory budget, then step down two or three notches and measure the metric drop. Choosing the largest model first and optimising cost later usually means redoing the work.
  5. Name the acceptable cases: large corpora of low individual value, pipelines where a reranker recovers some of the loss, and latency-critical online paths. Be conservative where a single miss is expensive, such as legal or clinical retrieval.
  6. Expected follow-up: can different documents use different dimensions? No. Every vector in an index must share one dimension, so changing it means rebuilding the whole index, the same migration cost as changing models.

  答题要点

  • 三笔账：存储与索引内存、检索延迟、检索质量，前两笔随维度近似线性，第三笔收益递减。
  • 套娃式表示让截短再归一化仍然可用，但一定有损失，损失多少要在自己的数据上评估。
  • 决策顺序是先按内存预算定上限，再往下试档位看指标掉多少。
  • 库大、单条价值低、后面还有重排兜底、对延迟敏感的场景，降维划算。
  • 同一索引里维度必须一致，改维度等于全库重建。

  Key points

  • Three costs: storage and index memory, query latency, and retrieval quality; the first two scale with dimensions, the third has diminishing returns.
  • Matryoshka representations make truncation viable, but it is lossy and the loss must be measured on your own data.
  • Decide by deriving a ceiling from the memory budget, then stepping down and measuring.
  • Truncation pays off for large corpora, low-value items, latency-sensitive paths, and pipelines with a reranker.
  • All vectors in one index share a dimension, so changing it forces a full rebuild.

D12 Agentic RAG：把检索做成工具，让模型自己决定查不查、查几次、要不要推翻重来

• 什么情况下你会拒绝把一个 RAG 系统做成 Agentic 的？拿什么数据说服你的团队？When would you refuse to make a RAG system agentic, and what data would you use to convince your team?

  国内高频海外高频进阶#agentic-rag#cost#engineering-judgement

  分析过程 · 先想清楚再作答

  1. 这题在考工程判断力，也在考你会不会算账。凡是答「Agentic 更先进所以要上」的，直接出局；面试官想听的是你能主动说出它的代价，并且用数字划出适用边界。
  2. 怎么拆：先承认收益来自哪一类问题，再看这类问题在你的流量里占多大比例。Agentic 的收益几乎全部集中在多跳和检索失败重试上，单文档可答的问题一次检索就够了，多查一轮纯属浪费。
  3. 所以判据不是感觉，是评估集：跑一遍，看 multi 那一档占多少题、涨了多少个点，再对照总调用次数涨了多少倍。在一份 20 题的集合上，我们量到的是多跳召回从 75% 涨到 100%，可答题整体只从 93.8% 涨到 100%，代价是平均检索调用从 1 次涨到 1.75 次、外加同样次数的自评调用——为 100% 的问题付钱，只有 5% 的问题拿到好处。
  4. 三类明确不上：延迟敏感（每多一轮就是一次检索加一次模型往返，首字延迟拉长一到两倍）；问题模式固定（九成是单文档可答，收益接近零）；成本吃紧（真实模型不像离线替身那样老实，成本方差比均值更难受，按均值做的容量规划会在长尾上被打穿）。
  5. 给出替代方案才算完整：分流。先用一次便宜的判断看这一问像不像多跳，像才进循环，不像走固定流程。九成走一次检索、一成走循环，账完全不一样。这也说明循环是一种能力，不是默认值。
  6. 可预期的追问是「那你怎么知道哪些问题像多跳」。答案是从评估集和线上日志里找模式（问句里同时问了两个事实、问的是某个角色背后的人），先用规则跑，跑不动再上小模型分类——顺序不要反。

  How to reason about it · think before answering

  1. This tests engineering judgement and whether you can do arithmetic. Anyone who says 'agentic is more advanced so we should ship it' is out. The interviewer wants you to name the cost and draw the boundary with numbers.
  2. Decompose it: identify which question types actually benefit, then check how much of your traffic they represent. Agentic gains concentrate in multi-hop questions and retrieval retries; single-document questions are answered by one lookup and every extra round is waste.
  3. So the criterion is the evaluation set, not intuition. On a 20-item set we measured multi-hop recall going from 75% to 100% while overall answerable recall moved only from 93.8% to 100%, at the cost of average retrieval calls going from 1 to 1.75 plus the same number of assessment calls - you pay for 100% of traffic so that 5% of it improves.
  4. Three clear refusals: latency-sensitive surfaces, where each round adds a retrieval plus a model round trip and roughly doubles time to first token; fixed question patterns, where nine in ten questions are single-document and the gain is near zero; and tight cost budgets, where a real model is less disciplined than an offline stand-in and the variance, not the mean, is what breaks your capacity plan.
  5. Finish with the alternative: route. Use one cheap check to decide whether a question looks multi-hop, and only then enter the loop. Nine tenths take a single retrieval, one tenth loops, and the economics change completely. Looping is a capability, not a default.
  6. Expected follow-up: how do you know which questions look multi-hop? Mine the eval set and production logs for patterns - two facts requested in one sentence, or a question about the person behind a role - start with rules, and reach for a small classifier only when rules stop working.

  答题要点

  • 收益集中在多跳与检索失败重试，单文档可答的问题上收益接近零。
  • 用评估集算账：multi 档涨了多少点，对照总调用次数涨了多少倍。
  • 实测过的一组数字：多跳召回 75% 到 100%，整体 93.8% 到 100%，检索调用 1 次到 1.75 次外加等量自评调用。
  • 三类不上：延迟敏感、问题模式固定、成本吃紧（方差比均值更难受）。
  • 替代方案是分流：便宜的判断先过滤，像多跳才进循环。
  • 循环是一种能力，不是默认值。

  Key points

  • Gains concentrate in multi-hop and retry cases; single-document questions gain almost nothing.
  • Settle it with the evaluation set: multi-hop delta against the multiplier on total calls.
  • One measured set: multi-hop recall 75% to 100%, overall 93.8% to 100%, retrieval calls 1 to 1.75 plus the same number of assessment calls.
  • Refuse when latency-sensitive, when question patterns are fixed, or when cost is tight - variance hurts more than the mean.
  • Route instead: a cheap check up front, and only multi-hop-looking questions enter the loop.
  • Looping is a capability, not a default.