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