面试题库
共 328 题,当前筛选 1 题。
课程全部30 天从前端工程师到 Agent 工程师5 天提示词工程零基础Claude 高效使用:从对话到 Claude CodeCodex 与 OpenAI Agents SDK 高效使用7 天 MCP:把工具接进任何 Agent7 天 Agent Skills:把经验做成可复用能力5 天上下文工程14 天 RAG:从检索到可信回答14 天用 Agent 搭一条 AI 短剧生产线
标签
全部#prioritization1#evaluation8#agentic-rag4#abstention3#cost3#chunking2#contextual-retrieval2#cost-tradeoff2#golden-set2#hybrid-search2#multi-hop2#observability2
还有 49 个标签收起标签
#query-rewriting2#access-control1#architecture1#bi-encoder1#caching1#content-hash1#coreference1#cost-optimization1#cross-encoder1#debugging1#embedding-migration1#engineering-judgement1#error-propagation1#evidence1#failure-modes1#faithfulness1#fallback1#filter-pushdown1#graph-rag1#hyde1#incremental-sync1#index-maintenance1#index-routing1#indexing1#intent-routing1#invalidation1#latency1#latency-budget1#llm-as-judge1#multi-tenancy1#multi-turn1#prompt-caching1#prompting1#query-transformation1#rag1#rank-fusion1#ranking1#reliability1#rerank1#retrieval-metrics1#retrieval-quality1#retrospective1#rollout1#scaling1#self-reflection1#stakeholder-communication1#system-design1#tool-design1#zero-downtime1
14 天 RAG:从检索到可信回答
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
如果预算只够做三件事来提升一个已有 RAG 系统的效果,你选哪三件?为什么是这三件?If you could only fund three changes to improve an existing RAG system, which three would you pick and why those three?
国内高频海外高频进阶#prioritization#evaluation#abstention分析过程 · 先想清楚再作答
- 这题在考优先级判断,而不是知识面。答成「上重排、上混合检索、上查询改写」这类手法清单几乎必然掉分——因为它跳过了一个前提:**你凭什么知道这三件对你的系统有用?** 面试官等的就是这句话。
- 所以第一件必须是**建评估**,而且理由要具体到不可反驳:没有秤,剩下两件做完你也说不清是变好还是变坏;有了秤,后面每一笔钱都能算回报。而且它便宜——检索侧三个指标是纯本地计算、几秒钟、零成本,能挂进每次提交;花时间的只是给题目标答案文档那一次。顺带说清评估集的配比:多跳与无答案各占一成以上,缺了无答案那一类,一个只会硬答的系统在报表上就是满分。
- 第二件是**把拒答从提示词搬进代码**,这一件的性价比通常最高而最容易被跳过。提示词里写十遍「找不到就说找不到」增益接近于零;而引用编号是一个闭集,判它存不存在只要一行代码,再加一道「这句话与被引块的实质重合度」就能拦住「编号是真的、内容是假的」那一类。我们实验里的基线拒答率是 0.0%——四道语料里根本没有答案的题一道都没闭嘴,这类缺陷在只报召回率的报表上完全不可见。
- 第三件要**先看失败案例再决定**,这才是这道题真正的答案。看完面板你会落到其中之一:多跳题占比高就补桥接检索或改索引结构;换个说法就捞不到,说明该上向量那一路或混合检索;答案捞到了却排不进上下文,那是重排或者预算的活。**先有失败案例,再有手法**——我们试过五种高级索引结构,没有一种跑赢基线,因为我们的系统压根没有那些结构要补的短板。
- 为什么不选那些看起来更亮的:Agentic 检索的收益集中在多跳题上而代价摊给全部问题;「全开」所有查询侧手法在我们的实测里召回率和默认配置一模一样,模型调用却是 2.5 倍、检索次数 4.3 倍。**堆手法很容易,说清楚为什么关掉某几项才是本事。**
- 可预期的追问:三件做完怎么证明钱花对了?答:每一项单独开关各跑一遍,报三笔账——指标涨了多少、延迟涨了多少、钱涨了多少。只报第一笔的提案不该被批准,包括你自己的。
How to reason about it · think before answering
- This tests prioritisation, not breadth. Answering with a list of techniques — add reranking, add hybrid retrieval, add query rewriting — almost always loses points, because it skips a prerequisite: how do you know those three help your system? That is precisely the sentence the interviewer is waiting for.
- So the first item has to be building evaluation, with a reason specific enough to be unarguable: without a scale, you cannot tell whether the other two helped or hurt; with one, every subsequent spend has a measurable return. It is also cheap — the three retrieval metrics are pure local computation, run in seconds, cost nothing, and can gate every commit; the only real effort is labelling answer documents once. Include the composition rule: multi-hop and unanswerable each above ten percent, because without the unanswerable class a system that only ever guesses scores perfectly on your report.
- Second, move abstention out of the prompt and into code — usually the best return per unit of effort, and the item most often skipped. Writing 'say you don't know' ten times in a prompt buys almost nothing. Citation numbers are a closed set, so checking existence is one line, and adding a substantive-overlap check catches the harder forgery where the number is real but the content is not. Our baseline abstention rate was 0.0 percent: four questions with no answer in the corpus, zero of them declined — a defect that is completely invisible on a report that only shows recall.
- Third, look at the failure cases before deciding, which is the actual answer to this question. After reading the panel you land on one of a few branches: a high share of multi-hop means bridging retrieval or a different index structure; queries that miss when phrased differently mean you need the vector route or hybrid retrieval; answers retrieved but never packed into context means reranking or budget. Failure cases first, technique second — we tried five advanced index structures and not one beat the baseline, because our system simply did not have the weakness they address.
- Why not the flashier options: agentic retrieval concentrates its gains on multi-hop while spreading cost across every question, and in our measurements turning on every query-side technique produced exactly the same recall as the default configuration while using 2.5 times the model calls and 4.3 times the retrievals. Stacking techniques is easy; explaining why you switched several off is the skill.
- Expected follow-up: once the three are done, how do you prove the money was well spent? Toggle each one individually and report three ledgers — how much the metric moved, how much latency moved, how much cost moved. A proposal that reports only the first should not be approved, including your own.
答题要点
- 第一件是建评估:没有秤,另外两件做完也说不清变好还是变坏;检索侧指标零成本可挂进每次提交。
- 评估集必须含无答案那一类,否则一个只会硬答的系统在报表上就是满分。
- 第二件是把拒答从提示词搬进代码:编号是闭集,再加实质重合度就能拦住「编号真、内容假」。
- 第三件由失败案例决定,不由手法清单决定——先有失败案例,再有索引结构或检索手法。
- 每一项单独开关跑一遍并报三笔账:指标、延迟、钱。只报第一笔的提案不该被批准。
Key points
- First, build evaluation: without a scale the other two changes are unverifiable, and the retrieval metrics are cheap enough to gate every commit.
- The golden set must include unanswerable questions, or a system that only ever guesses scores perfectly on your report.
- Second, move abstention from the prompt into code: citation numbers are a closed set, and a substantive-overlap check catches real-number-fake-content forgeries.
- Third is chosen by the failure cases, not by a list of techniques — failure cases first, index structure or retrieval trick second.
- Toggle each change individually and report three ledgers: metric, latency, cost. A proposal reporting only the first should not be approved.