When an interviewer digs into a line on your resume, the follow-up questions come from a small, predictable set of directions. This page lists five of them, shows a real example of each, explains what each one is testing, and ends with a self-check you can run on every “I owned X” line before the interview.
面试官追问你简历上的某一条经历时，方向其实是固定的几种。本页列出 5 类追问、每类一个真实原文、它在拆穿什么、怎么接，最后附一份可以自己跑一遍的自查清单。中文版在下方。
Interviews scored on fixed dimensions with one rating scale (structured interviews) predict job performance better than free conversation. In the most-cited meta-analysis, the corrected operational validity was about 0.51 for structured interviews versus 0.38 for unstructured ones (Schmidt & Hunter, 1998). A 2022 re-analysis with more conservative corrections lowered both numbers (0.42 vs 0.19) but kept the direction (Sackett, Zhang, Berry & Lievens, 2022).
A follow-up is how an interviewer turns a smooth story into something scoreable: they keep digging along one dimension until they get a concrete detail. Fluency earns nothing on its own; checkable facts do. So the thing to prepare is not a smoother paragraph but the detail underneath each claim.
| Kind | What it is testing | Real example | How to answer |
|---|---|---|---|
| 1 · Choice rationale | Did you actually weigh options, or just learn a name? | “When fine-tuning the model, why did you choose LoRA rather than another fine-tuning method?” | Name at least two alternatives that were on the table, what each would have cost, and the specific reason you ruled them out. “We used the standard approach” earns another follow-up. |
| 2 · Implementation detail | Did you build it, or do you know the concept? | “You said the compositing engine was built in-house on FFmpeg. How exactly does it recover state?” | Go down to steps, interfaces or data structures. Re-describing the feature is not an answer; the question is about the gap between the feature and how it works. |
| 3 · Personal scope | Is “I was responsible” hands-on, or nearby? | “In that enterprise knowledge-base project, did you personally own the document-parsing step? Why were you the one leading it?” | Swap “we” for “I” and draw the line around what you delivered yourself. Saying “a colleague built that part” makes you more credible, not less. |
| 4 · Metrics | Was the result measured or estimated? | “You said you delivered the whole front-end experience. How did you measure and improve it? Which metrics?” | Give the metric you actually watched, how it is defined, and one change that moved it and by how much. A number without its definition invites “where does that number come from?” |
| 5 · Business understanding | Do you understand the setting, or are you reciting? | “In the compliance preparation you owned, what were the key steps, and how did you order them?” | State the order first and why step one comes first. Skipping the order and jumping to conclusions gets you asked to rebuild it. |
The example questions are translated from a recorded session of an AI interviewer (MockLive, 28 September 2026, 10 rounds). The grouping into five kinds is our summary of that session, not an exhaustive taxonomy, and the “how to answer” column is advice rather than a tested result.
Any line where you get stuck is the line the real interview will dig into. Fix that line first instead of memorising the whole story again: rehearsing polishes the narrative, filling the gap adds the detail.
What this checklist is not. It is a preparation aid, not a lie detector. Published research has not shown that follow-up questions expose fabricated answers; one experiment found that follow-ups actually increased faking (Levashina & Campion, 2007). Use it to close gaps in your own story before you speak.
Reading a checklist is not the same as answering under questioning. MockLive is a voice mock interview tool that reads your resume and the job description and asks exactly these kinds of follow-ups; its report quotes your own words back to you and lets you replay the audio. The first session is free. Background reading: why it helps to get stumped before the real interview.
Disclosure: this checklist is published by the team that builds MockLive. The method itself works without any product.
按固定维度、统一量表打分的结构化面试，对工作表现的预测力高于自由聊天式面试：经典元分析给出的校正后操作效度约为 0.51 对 0.38（Schmidt & Hunter, 1998）；2022 年更保守的重算下修为 0.42 对 0.19，方向不变（Sackett et al., 2022）。
追问就是面试官把一段流利叙述变成「可以打分的事实」的动作：沿一个维度往下挖，直到拿到具体细节。答得流利不加分，答得出可核对的细节才加分。所以要准备的不是更顺的整段话，而是每个主张底下的细节。
| 追问类型 | 它在拆穿什么 | 真实追问原文 | 怎么接 |
|---|---|---|---|
| 1 · 选型理由 | 你是真做过取舍，还是只背了个名词 | 你能解释一下，在微调大模型时，为什么选择LoRA，而不是其他微调方法？ | 说出当时摆在桌上的至少两个备选、各自的代价，以及你排除它们的具体理由。只说「我们用主流方案」会被再追一层。 |
| 2 · 实现细节 | 你是写过，还是只知道概念 | 你刚才提到后期合成引擎基于FFmpeg自研，能具体说说这个自研方案是如何实现状态恢复的吗？ | 给到一个具体到步骤、接口或数据结构的路径。复述一遍功能不算回答——追问问的就是功能和实现之间那段。 |
| 3 · 个人边界 | 「我负责」是亲自做，还是在旁边看 | 你提到RAG和私有化部署，那具体在一个企业知识库项目里，你个人负责文档解析的步骤吗？为什么是你来主导这一块？ | 把「我们」换成「我」，划清哪一段是你独立交付的。主动说明「这部分是同事做的」反而更可信。 |
| 4 · 量化指标 | 成果是测出来的，还是估出来的 | 你提到“前端体验全部交付”，能具体说一下，你是如何衡量和优化用户交互体验的？用过哪些具体指标？ | 报出你实际盯的那个指标、它的口径，以及你做过哪次优化让它动了多少。只给数字不给口径，会被追问「这个数怎么来的」。 |
| 5 · 业务理解 | 你懂这个场景，还是在背书 | 你负责合规材料准备中，具体有哪几个关键步骤，是怎样按优先顺序来的？ | 先说明顺序、以及第一步为什么是它。跳过顺序直接讲结论，会被要求重排一遍。 |
追问原文逐字摘自 MockLive 2026-09-28 的一场 10 轮实测（面试官为产品引擎输出）；「5 类」是基于这场实测的归纳，不是穷尽分类；「怎么接」是方法建议，未经对照验证。完整实录见这场实测的 10 轮追问链与评分卡，原文与更多说明见面试被追问怎么接。
任何一条卡住，就是真面试里会被追到的那一处。先补这一条，而不是把整段经历再背一遍：背熟的是叙述，补上的是细节。
这份清单不是什么。 它是准备工具，不是测谎工具。已发表研究并没有证明追问能识别编造的作答；Levashina & Campion（2007）的实验反而发现追问提高了作答者的欺骗程度（DOI）。它的用途是让你在开口之前先补上细节短板。
看懂清单不等于被追问时答得出来。MockLive（AI 模拟面试）会读你的简历和目标岗位 JD，按上面这几类方向逐轮追问；报告逐字引用你的原话，可回放当时的录音。首场免费。
披露：本清单由 MockLive 团队发布；清单里的方法本身不依赖任何产品。
License: CC BY 4.0. You may copy, adapt and share this checklist with attribution.