面试题库

30 天从前端工程师到 Agent 工程师

D4 模型接入与系统提示词：多 provider 抽象与 fallback、覆盖默认人设（dg P03/P04/M04）

• 系统提示词（system prompt）在 Agent 里起什么作用？为什么不能用框架默认的？What does the system prompt do in an agent, and why not rely on the framework default?

  国内高频海外高频进阶#prompt-engineering#system-prompt

  分析过程 · 先想清楚再作答

  1. 前半句是送分题，后半句才是区分度所在——很多人答得出 system prompt 是干什么的，答不出「默认值有什么坑」。
  2. 先说作用：它是唯一一段在整段对话里权重稳定、不会被后续几十轮稀释的指令，用来设定身份、能力边界和输出格式。
  3. 再答「为什么不能用默认的」，要给出三条具体后果而不是泛泛说「不够定制」：一是它不知道你的业务边界，用户问业务外的问题它会热情地答；二是它不约束输出格式，前端样式会被冷不丁冒出的 Markdown 标题打乱；三是最要命的——它会随框架升级而变化，你测好的所有行为建立在一段看不见的文本上，出 bug 时你的代码一行没动，极难排查。
  4. 结论落到工程做法：生产环境的 system prompt 是拼出来的模板，结构是「人设 + 能力边界 + 输出要求 + 动态上下文」，最后一块每次请求现拼。
  5. 可以预期的追问：动态上下文里最容易漏什么？答「当前时间」——模型没有时钟，不告诉它今天几号，它算不出「三天前下的单」是哪天。这个细节很能体现有没有真做过。
  6. 第二个追问：prompt 怎么测试？答案是把它当配置而不是代码——存库、加版本号、支持按比例灰度，因为你没法写单元测试断言「模型语气变友好了」。

  How to reason about it · think before answering

  1. The first half is a warm-up; the discriminating half is why the default is dangerous.
  2. State the role: it is the one instruction block whose weight stays stable across dozens of turns, setting identity, capability boundaries and output format.
  3. Then give three concrete consequences rather than 'not customized enough': it does not know your business boundary so it happily answers off-topic questions; it does not constrain output format so stray Markdown headings break your UI; and worst, it changes when the framework updates — your tested behavior rests on invisible text, and the bug appears with zero code changes.
  4. Land on practice: a production system prompt is assembled from a template — persona, capability boundary, output requirements, dynamic context — with the last part rebuilt per request.
  5. Expect: what gets forgotten in dynamic context? The current time. Models have no clock; without today's date they cannot resolve 'the order I placed three days ago'.
  6. Second follow-up: how do you test a prompt? Treat it as configuration, not code — store it, version it, roll it out to a percentage, because you cannot unit-test 'the tone got friendlier'.

  答题要点

  • system prompt 设定身份、能力边界与输出格式，是对话里权重最稳定、不被后续轮次稀释的一段指令
  • 框架默认人设不知道你的业务边界，会热情回答业务外的问题，浪费 token 且跑题
  • 默认人设不约束输出格式，模型可能吐出 Markdown 标题打乱前端样式
  • 最危险的是默认值会随框架升级而变化，代码一行没动却出现行为回归，极难排查
  • 生产做法：显式拼模板（人设 + 能力边界 + 输出要求 + 动态上下文），当作配置存储、加版本号、可灰度

  Key points

  • The system prompt sets identity, capability boundaries and output format, and keeps stable weight across turns
  • A default persona does not know your business boundary and will cheerfully answer off-topic questions
  • It does not constrain formatting, so stray Markdown can break your UI
  • Most dangerous: defaults change on framework upgrades, producing behavior regressions with no code change
  • Production practice: assemble it explicitly, treat it as versioned configuration, and roll changes out gradually

D5 工具系统与事件驱动：参数校验、错误回传让模型自纠错、事件订阅（dg P05/P06/M05/M07）

• 设计 Agent 的工具时你会遵循哪些原则？名字、描述、参数分别该怎么写？What principles do you follow when designing tools for an agent — how do you write the name, the description and the parameter schema?

  国内高频海外高频基础#tool-design#prompt-engineering

  分析过程 · 先想清楚再作答

  1. 这题的区分度在于你把工具描述当成什么。当成函数注释的人会答「写清楚做什么」，当成提示词的人才会答到点子上——描述会原样进入模型的上下文，参与「该不该调、参数填什么」的判断，它的读者是模型不是同事。
  2. 拆成三件事分别说：名字要动词加宾语（query_order 而不是 handler2），因为名字是模型的第一道筛选；描述最有价值的一句不是「做什么」而是「什么时候不该用它」，把边界写进去能砍掉一大半误用；参数里每个字段都要有自己的 description，格式类字段还要给一个合法示例——模型对「订单号」没有概念，看到 SO20260901 这个样例，填对的概率会陡增。
  3. 接着给出一条几乎没人主动说的成本判断：工具定义每一轮都会被完整重发，一个写得扎实的工具约 100 到 150 token，挂 20 个就是每轮两三千 token 的固定开销。所以「工具越多越强」是错的，只挂当前场景用得上的那几个。
  4. 再补一条可迁移的工程判断：工具改名或改语义是破坏性变更，等价于换了个工具——调好的提示词会失效，历史会话的 messages 里还留着旧名字，恢复旧会话时模型会去调一个不存在的工具。所以改工具要像改公开 API 一样走版本与灰度。
  5. 可以预期的追问：几十上百个工具怎么办？答案是先用一轮便宜模型做工具检索，只把最相关的几个塞进正式请求，而不是一股脑全挂上。

  How to reason about it · think before answering

  1. The discriminator is what you think a tool description is. People who treat it as a docstring answer 'describe what it does'; people who treat it as part of the prompt get it right — the description goes verbatim into the model's context and drives both tool selection and argument filling. Its reader is the model, not your teammate.
  2. Split it into three: names read like commands (query_order, not handler2) because the name is the model's first filter; the most valuable sentence in a description is not what the tool does but when NOT to use it, which removes most misrouting; and every parameter needs its own description plus a concrete example for format-shaped fields — a model has no notion of 'order id', but SO20260901 makes it far more likely to get it right.
  3. Then raise the cost point most candidates miss: tool definitions are resent in full every turn. A well-written tool runs 100 to 150 tokens, so twenty of them is a fixed two- to three-thousand-token tax per turn. More tools is not more capable — only mount what the current scenario needs.
  4. Add a transferable engineering judgment: renaming a tool or silently widening its semantics is a breaking change. Tuned prompts stop working, and old sessions still carry the old name in messages, so resuming one makes the model call a tool that no longer exists. Version and roll out tool changes the way you would a public API.
  5. Expect the follow-up: what about dozens or hundreds of tools? Retrieve tools with a cheap model first and mount only the top few, rather than shipping the whole catalog every turn.

  答题要点

  • 工具定义是提示词的一部分，读者是模型：它只能看到名字、描述、参数 schema，看不到你的实现
  • 名字用动词加宾语；描述里最值钱的是「什么时候不该用它」；每个参数都要有 description，格式类字段给一个合法示例
  • 工具定义每轮完整重发，20 个工具就是每轮固定两千多 token，只挂当前场景用得上的
  • 改名或改语义等于换工具，会让调好的提示词失效、让旧会话调到不存在的工具，要走版本与灰度
  • 工具规模上去之后，先用便宜模型做工具检索再挂载最相关的几个

  Key points

  • A tool definition is part of the prompt; the model only sees name, description and parameter schema
  • Name it verb plus object; the most valuable line in a description is when not to use it; every parameter needs a description, and format fields need a concrete example
  • Definitions are resent every turn, so twenty tools is a fixed two- to three-thousand-token tax — mount only what the scenario needs
  • Renaming or redefining a tool is a breaking change that invalidates tuned prompts and breaks resumed sessions
  • At scale, retrieve the relevant tools with a cheap model before mounting them

5 天上下文工程

D1 上下文是最稀缺的资源：窗口、注意力衰减与成本，从提示词工程走到上下文工程

• 提示词工程和上下文工程的分界在哪？什么时候该从前者切换到后者？Where is the line between prompt engineering and context engineering, and when do you switch?

  国内高频海外高频进阶#prompt-engineering#context-engineering#scoping

  分析过程 · 先想清楚再作答

  1. 这题最容易答成「上下文工程是提示词工程的升级版」，那是营销话术。面试官想听的是一条能当场用来分类问题的判据。
  2. 怎么拆：先给对象的差别。提示词工程处理的是内容——一段话怎么写才准确；上下文工程处理的是预算分配——整只箱子里各块占多少、什么时候该扔。前者是单次调用内的优化，后者是跨多轮的状态管理。
  3. 再给一条现场可用的分类法，用症状反推：同一个问题问一次答错、换个说法就对，是提示词问题；前五轮正常、第二十轮开始违反最初约束，是上下文问题；明明查到了数据模型却说没有，也是上下文问题——信息在窗口里，只是被淹没了。
  4. 结论：切换的时机不是「提示词写得够好了」，而是「问题的成因从单次表达变成了多轮累积」。加了工具、加了检索、开始多轮长跑，这三件事任何一件发生，都意味着该切换了。
  5. 可预期的追问：那上下文工程包含提示词工程吗？答：系统提示是上下文四块里的一块，所以提示词工程是上下文工程的一个子问题，但它解决不了另外三块——工具定义、历史和工具结果都不是靠把话写好能管住的。

  How to reason about it · think before answering

  1. The trap is answering that context engineering is just prompt engineering leveled up. Interviewers want a rule they can apply to classify a live problem.
  2. Start with the object of each. Prompt engineering shapes content: how to phrase one instruction precisely. Context engineering allocates budget: how much of the window each part gets and when to drop things. One optimizes inside a single call, the other manages state across turns.
  3. Then give a symptom-based test. Wrong once but right after rephrasing means a prompt problem. Fine for five turns and violating the original constraints by turn twenty means a context problem. Retrieving the data and then claiming it does not exist is also a context problem: the information is in the window but buried.
  4. Conclusion: you switch not when the prompt is good enough, but when the cause moves from single-turn phrasing to multi-turn accumulation. Adding tools, adding retrieval, or running long sessions each trigger the switch.
  5. Expect the follow-up: does context engineering subsume prompt engineering? The system prompt is one of the four parts, so prompt engineering is a subproblem, but it cannot touch tool definitions, history, or tool results.

  答题要点

  • 提示词工程处理内容，上下文工程处理预算分配；一个在单次调用内，一个跨多轮。
  • 症状分类法：换个说法就对是提示词问题；跑久了开始违反约束是上下文问题；查到了却说没有也是上下文问题。
  • 切换时机是问题成因从单次表达变成多轮累积，通常发生在加工具、加检索、开始长跑之后。
  • 系统提示只是上下文四块之一，所以写好提示词管不住另外三块。

  Key points

  • Prompt engineering shapes content; context engineering allocates budget across turns.
  • Classify by symptom: fixed by rephrasing is a prompt issue; drifting after many turns is a context issue; retrieved but reported missing is also a context issue.
  • Switch when the cause moves from single-turn phrasing to multi-turn accumulation, typically after adding tools, retrieval, or long-running sessions.
  • The system prompt is one of four parts, so good phrasing alone cannot control the other three.

14 天 RAG：从检索到可信回答

D6 生成这一侧：上下文怎么排、引用怎么标、什么时候必须拒答，以及流式回答

• 上下文里材料的排列顺序会影响回答质量吗？如果会，你会怎么排？Does the ordering of retrieved passages in the context affect answer quality? If so, how would you order them?

  国内高频海外高频基础#context-assembly#prompt-engineering#ordering

  分析过程 · 先想清楚再作答

  1. 这是一道送分题，但答成「按相关性从高到低排」就只拿到一半分。面试官想听的是你知不知道位置本身是个变量。
  2. 结论先说：会影响。模型对上下文开头和结尾的材料明显更敏感，正中间的最容易被读漏。所以简单按分数从高到低顺排，等于把第二重要的材料放进了最不容易被读到的位置。
  3. 给出排法：第 1 名放开头、第 2 名放结尾、第 3 名放第二位、第 4 名放倒数第二位，依次往里收。这样按分数排下来越靠中间的块本来就越不重要，被读漏的代价最小。
  4. 顺带把排序之外的三道手续说全，显得你真的写过这段代码：同分要有决胜键（否则块编号会在两次运行之间飘，日志对不上）、要按归一化文本去重（同一段话常在手册和问答里各出现一次）、要有 token 预算并且塞不下时不要直接停。
  5. 可预期的追问：这个结论怎么验证？答案是别猜——固定一批问题，只改排列顺序跑对照，看指标差多少。位置效应在不同模型、不同上下文长度上强弱不一样，把它当成一个要在自己数据上量的参数，而不是一条普适定律。

  How to reason about it · think before answering

  1. This is a warm-up question, but 'sort by relevance descending' only earns half the credit. The interviewer wants to know whether you treat position itself as a variable.
  2. State the conclusion first: it does matter. Models attend more reliably to material at the start and the end of the context, and are most likely to miss what sits in the middle. Plain descending order therefore parks your second-best passage in the worst spot.
  3. Give the ordering: rank one first, rank two last, rank three second, rank four second-to-last, folding inward. Whatever ends up in the middle is by construction the least important, so the cost of it being skipped is smallest.
  4. Round it out with the other assembly steps, which shows you have written this code: a deterministic tiebreaker (otherwise block numbers drift between runs and your logs stop matching), dedupe on normalised text, and a token budget that skips rather than stops when a block does not fit.
  5. Expected follow-up: how would you verify this? Do not guess. Hold the question set fixed, vary only the ordering, and measure. Position effects differ by model and context length, so treat it as a parameter to measure on your own data rather than a universal law.

  答题要点

  • 会影响：开头和结尾的材料更容易被用上，正中间的最容易被读漏。
  • 排法是最重要的放两端：第 1 名开头、第 2 名结尾、第 3 名第二位，依次往里收。
  • 组装还要做三件事：同分给决胜键保证编号稳定、按归一化文本去重、控 token 预算且塞不下时跳过而不是终止。
  • 位置效应的强弱因模型与上下文长度而异，要在自己的数据上做对照实验量出来，不能当普适定律照搬。

  Key points

  • Yes: material at the head and tail is used more reliably, the middle is most often skipped.
  • Put the strongest at both ends: rank one first, rank two last, rank three second, folding inward.
  • Assembly also needs a deterministic tiebreaker for stable numbering, dedupe on normalised text, and a token budget that skips oversized blocks instead of stopping.
  • The strength of the effect varies by model and context length, so measure it on your own data instead of quoting it as a law.