Interview Bank

Mastering Claude: From Conversation to Claude Code in 5 Days

D1 Advanced Prompting and Claude's "Personality": System Prompt, XML Tags, Letting the Model Think First, Structured Output

• What belongs in a system prompt and what doesn't? If the model keeps ignoring one rule, what do you check first?system prompt 应该放什么、不该放什么？如果一条规则模型总是不遵守，你会先检查什么？

  Common in ChinaCommon overseasBasic#system-prompt#prompt-design

  How to reason about it · think before answering

  1. The question tests boundaries, not writing skill. Naming what to exclude, and why, is what separates a strong answer.
  2. Give the rule: the system prompt is a fixed premise resent on every request, so it holds only what is true for the whole conversation — role, constraints as prohibitions, output style. Anything that varies per turn belongs in the user message.
  3. Then the anti-patterns: obvious conventions, pasted API docs, and per-turn material dilute the important rules and also invalidate the prompt-cache prefix on every call.
  4. Debug order for an ignored rule: check length first and prune, then check for ambiguity or conflicting rules, and only then add emphasis. If the rule is a must-run action, move it to a deterministic gate instead of adding more words.
  5. Likely follow-up: can system go last? Possible but unwise — earlier instructions carry more weight and a moving prefix breaks caching.

  分析过程 · 先想清楚再作答

  1. 这题考的是「职责边界」而不是「会不会写」。答成「放角色和要求」是及格线，能说出「不该放什么」以及「为什么」才有区分度。
  2. 先给一条判据：system prompt 是每次请求都重发的固定前提，所以只放整场对话都成立的东西——角色、边界（禁止项）、输出风格；每次都变的（时间、用户名、本轮材料）放 user 消息。
  3. 再说反面：把模型本来就知道的常识（「写干净的代码」）、大段 API 文档、每轮都不一样的材料塞进 system，只会稀释真正重要的规则，还会让 prompt caching 的前缀每次都变。
  4. 「规则总是不遵守」的排查顺序：先看 system 是不是太长导致规则被淹没（删到不能再删），再看规则是否含糊或与别的规则冲突，最后才考虑加强调；如果是「每次必须执行」的动作，应该改成程序层面的门禁而不是继续加规则。
  5. 可预期的追问：system 放最后行不行？可以但不推荐——模型对靠前的指令更敏感，且会破坏缓存前缀。

  Key points

  • Include role, prohibitions, and output style — premises that hold for the whole conversation
  • Exclude volatile facts, common sense the model already has, and long pasted docs
  • The system prompt is resent every request: longer means costlier and rules get buried
  • For an ignored rule: prune first, disambiguate second, emphasize last; must-run actions become deterministic gates

  答题要点

  • 放：角色、边界（写禁止项）、输出风格；整场对话都成立的固定前提
  • 不放：会变的信息（时间、用户名、本轮材料）、模型本来就知道的常识、大段文档
  • system 每次请求重发，越长越贵，也越容易让关键规则被淹没
  • 规则不被遵守先删再改再强调；「每次必须做」的动作改成程序门禁

D2 Long Documents, Multimodal Input, and a First Look at the API: Using Large Context, Saving Money With Prompt Caching, PDF and Image Input, Citation-Backed Answers; a Minimal Messages API Call

• Why is the context window called the scarcest resource in LLM applications? With million-token windows, does that still hold?为什么说上下文窗口是 LLM 应用里最稀缺的资源？窗口已经有一百万 token 了，这个说法还成立吗？

  Common in ChinaCommon overseasBasic#context-window#cost

  How to reason about it · think before answering

  1. The second sentence is the point. 'The window has a limit' is a dated answer; explain why scarcity survives large windows.
  2. Three causal chains: models are stateless so every request re-reads the whole input and bills it, a big window only solves fitting, not re-sending; longer context means more latency and diluted attention, so adherence to early instructions degrades as the window fills; and in agent workflows every file read and command output lands in the same window, filling it far faster than chat does.
  3. Conclusion: scarcity shifted from 'won't fit' to 'every token costs money and attention', so the discipline becomes active management — include only what is needed, cache the stable prefix, delegate research to subagents with their own context, and clear between tasks.
  4. Production math: a 60-page PDF is roughly 100k tokens; ten questions about it are a million input tokens; caching versus not caching is an order of magnitude apart.
  5. Follow-up: when should context accumulate? While deep in one complex problem where the history is still load-bearing; the test is whether the next step will use it.

  分析过程 · 先想清楚再作答

  1. 题眼在第二句。只答「窗口有上限」已经过时了，面试官想听的是「窗口变大之后为什么还稀缺」。
  2. 从三条因果链推：一、模型无状态，每次请求都把全部输入重读一遍，输入 token 按次计费——窗口大只解决了放得下，没解决每次都要重搬；二、上下文越长，延迟越高、注意力越稀释，模型对早期指令的遵守度会下降，也就是「性能随填充度下降」；三、Agent 场景里每读一个文件、每跑一条命令的输出都进同一个窗口，填得比聊天快得多。
  3. 结论：窗口大了，稀缺性从「放不下」变成了「每一 token 都在花钱和稀释注意力」，所以管理手段变成了主动管：只放必要的、把不变的缓存起来、把查资料的活派给独立上下文的子代理、该清就清。
  4. 生产视角：算一笔账——60 页 PDF 约 10 万 token，围着它问 10 个问题就是 100 万输入 token；不用缓存和不用缓存的差价是一个量级。
  5. 可预期的追问：那什么时候应该让上下文积累？在一个复杂问题里深挖时历史是有价值的；判据是「这段历史下一步还会不会用到」。

  Key points

  • Models are stateless: every request re-reads and bills the full input; a large window solves fitting, not re-sending
  • Longer context raises latency and dilutes attention; adherence to early instructions drops
  • Agent workflows dump every file read and command output into the same window
  • Tactics: include only what's needed, cache the stable prefix, isolate research in subagents, clear between tasks

  答题要点

  • 模型无状态，每次请求重读全部输入并计费；窗口大只解决放得下，不解决每次重搬
  • 上下文越长延迟越高、注意力越稀释，早期指令遵守度下降
  • Agent 场景每次读文件、跑命令的输出都进窗口，填得比聊天快得多
  • 对策：只放必要的、缓存不变前缀、用子代理隔离查资料、任务之间清空

D3 Getting Started With Claude Code and Managing Context: Install, Writing CLAUDE.md and "Trim Until You Can't", Permission Modes, Plan Mode's "Explore, Then Plan, Then Write", /clear /compact /rewind, Giving Claude a Verifiable Check

• How do CLAUDE.md and skills divide responsibilities, what goes where, and what goes wrong when CLAUDE.md grows to 500 lines?CLAUDE.md 和 skill 的分工是什么？什么内容该放哪边？一份 CLAUDE.md 写到 500 行会出什么问题？

  Common in ChinaCommon overseasBasic#claude-md#skills#context

  How to reason about it · think before answering

  1. This tests context-cost awareness. 'CLAUDE.md holds rules, skills hold procedures' is the conclusion; derive it from how each is loaded.
  2. Start from load timing: CLAUDE.md enters context in full every session — a fixed cost; a skill keeps only its one-line description resident and loads its body on invocation — a variable cost. Hence short facts that always apply go in CLAUDE.md, occasional multi-step procedures go in skills.
  3. Give the table: commands, non-default style, repo etiquette, environment quirks, and the definition of done belong in CLAUDE.md; deployment runbooks, issue-fixing steps, document generators belong in skills. Multi-step procedures in CLAUDE.md or always-on rules inside a skill are both misplacements.
  4. At 500 lines the failure is dilution, not capacity: important rules drown, adherence drops, and every turn pays for the bloat. Fixes: prune ruthlessly (would removing this cause a mistake?), move occasional content to skills, split path-scoped rules into .claude/rules/ so they load only when matching files are touched.
  5. Follow-ups: a rule that keeps being ignored — prune, then disambiguate, then emphasize; anything that must run every time should be a hook, not a sentence.

  分析过程 · 先想清楚再作答

  1. 这题考的是「上下文成本意识」。答成「CLAUDE.md 放规则、skill 放流程」只是结论，面试官想听你从加载方式推出这个结论。
  2. 拆法从加载时机入手：CLAUDE.md 每次会话整份进入上下文，是固定成本；skill 只有描述那一行常驻，正文在被触发（模型判断相关或用户输入 /name）时才加载，是按需成本。所以「每次都成立的短事实」放 CLAUDE.md，「偶尔才用、一用就是多步」的流程放 skill。
  3. 给判据表：命令、风格差异、仓库礼仪、环境怪癖、完成的判据进 CLAUDE.md；部署流程、修 issue 的固定步骤、某类文档的生成方法进 skill。反过来，CLAUDE.md 里出现了多步流程，或 skill 里放了「每次都要遵守」的规则，都是放错了。
  4. 500 行的问题不是「太长跑不动」，而是稀释：重要规则被淹没，模型的遵守度反而下降，还白白吃掉每轮的窗口。对策是「删到不能再删」（删掉会不会让它犯错？不会就删）、把偶尔用的挪进 skill、按路径拆进 .claude/rules/ 只在碰到匹配文件时加载。
  5. 可预期的追问：「规则它老是不听怎么办」——先删再改再强调；「必须每次执行」的动作根本不该靠 CLAUDE.md，要改成 hook。

  Key points

  • CLAUDE.md loads in full every session — fixed cost; a skill keeps one line resident and loads on demand
  • CLAUDE.md: short always-true facts — commands, style deltas, etiquette, definition of done; skills: occasional multi-step procedures
  • Bloat dilutes: key rules drown, adherence drops, every turn pays
  • Fixes: prune, move occasional content to skills, split path-scoped rules; must-run actions become hooks

  答题要点

  • CLAUDE.md 每次会话整份加载，是固定成本；skill 只常驻一行描述，正文按需加载
  • CLAUDE.md 放每次都成立的短事实：命令、风格差异、规矩、完成判据；skill 放偶尔用的多步流程
  • 写长的后果是稀释：重要规则被淹没、遵守度下降、每轮白付窗口
  • 对策：删到不能再删、偶尔用的进 skill、按路径拆进 rules；必须每次做的改成 hook

D4 Extending Claude Code: Hooks (Deterministic) vs. CLAUDE.md (Advisory), Skills, Subagents, Plugins, Wiring Up an MCP Server, CLI Tools First

• Why are hooks more reliable than rules in CLAUDE.md? What belongs in each? Give one rule you would move from CLAUDE.md to a hook.为什么 hooks 比 CLAUDE.md 里的规则更可靠？各适合放什么？举一个你会从 CLAUDE.md 挪到 hook 的例子。

  Common in ChinaCommon overseasBasic#hooks#claude-md

  How to reason about it · think before answering

  1. This tests the systemic position of advisory versus deterministic, not feature recall. 'Hooks are scripts that run automatically' is a description; explain why model adherence is not program execution.
  2. Breakdown: CLAUDE.md enters the model's context as text and the model decides after reading — adherence is high but not total, drops as the file grows, and can be lost after compaction. A hook is a script Claude Code itself runs unconditionally at fixed lifecycle points (PreToolUse, PostToolUse, Stop), with the exit code deciding whether to block, independent of the model's judgment.
  3. One-line rule: actions that allow zero exceptions become hooks; preferences that usually apply stay in CLAUDE.md. The inverse also holds — delete rules the model follows by default, convert must-always rules into hooks, and the file shrinks.
  4. Make the example concrete: 'run lint and tests before committing' is occasionally skipped as text; as a Stop hook, failing tests exit 2 and the model receives the summary and keeps fixing. 'Never edit migrations/' becomes a PreToolUse hook matching Edit|Write that exits 2 on a path hit.
  5. Follow-ups: risks? Hooks are code running on your machine — a cloned repo's hooks execute, and headless mode shows no trust dialog; a Stop hook is overridden after 8 consecutive blocks to prevent loops.

  分析过程 · 先想清楚再作答

  1. 这题考的是「建议 vs 确定性」的系统位置，不是背功能名。答「hooks 是自动执行的脚本」只是描述，要说清为什么模型的遵守率不等于程序的执行率。
  2. 拆法：CLAUDE.md 的内容作为文字进入模型上下文，由模型读后决定怎么做——遵守率高但不是百分之百，文件越长越低，压缩后还可能丢失。hook 是 Claude Code 程序在固定生命周期点（PreToolUse / PostToolUse / Stop 等）无条件运行的脚本，由退出码决定拦不拦，与模型的判断无关。
  3. 判据一句话：一次例外都不能有的动作做成 hook；通常应该这样的偏好写进 CLAUDE.md。反向操作也成立：CLAUDE.md 里模型已经默认遵守的删掉，必须百分之百的换成 hook，文件就短了。
  4. 例子要具体：「提交前跑 lint 与测试」——作为文字它偶尔会被跳过；做成 Stop hook，测试不过 exit 2，模型收到失败摘要继续修，直到通过；「不许改 migrations/」做成 PreToolUse hook 匹配 Edit|Write，路径命中就 exit 2。
  5. 可预期的追问：hook 有没有风险？有——它是代码，跑在你机器上，clone 陌生仓库时别人的 hook 会执行，无头模式没有信任对话框；Stop hook 连续 8 次阻止后会被放行防死循环。

  Key points

  • CLAUDE.md is text the model reads and then decides on — high but not total adherence
  • A hook is a script the program runs unconditionally at lifecycle points; the exit code decides, not the model
  • Zero-exception actions become hooks; usual preferences stay in CLAUDE.md
  • Examples: pre-commit tests as a Stop hook; a migrations deny as a PreToolUse hook

  答题要点

  • CLAUDE.md 是送进上下文的文字，由模型读后决定，遵守率高但不是百分之百
  • hook 是程序在固定生命周期点无条件跑的脚本，退出码决定拦不拦，与模型判断无关
  • 一次例外都不能有的做 hook；通常应该这样的写 CLAUDE.md
  • 例：提交前测试改成 Stop hook；禁改 migrations 改成 PreToolUse hook

D5 Automation and Scale: Headless -p Into CI, Parallel Sessions and Worktrees, Writer/Reviewer Dual Sessions, Adversarial Review, Common Failure Modes; a 20-Line Minimal Agent SDK Agent

• When do you use the Claude Agent SDK versus the Messages API directly, and how do both relate to claude -p?Agent SDK 和直接调 Messages API 各适合什么场景？它们和 claude -p 是什么关系？

  Common in ChinaCommon overseasBasic#agent-sdk#messages-api

  How to reason about it · think before answering

  1. This tests layered understanding: all three entry points share one model; the difference is who supplies the loop and the tools. 'The SDK is higher level' says nothing.
  2. Messages API (@anthropic-ai/sdk / anthropic): one request, one response; you define tools, write the loop, manage context. Fits Q&A, extraction, classification, structured output, cited document Q&A, and custom agents where you want full control of the loop.
  3. Agent SDK (@anthropic-ai/claude-agent-sdk / claude-agent-sdk): Claude Code packaged as a library — built-in Read/Edit/Bash/Glob/Grep, the full agent loop, context management, permissions, hooks, subagents, sessions. You pass a task and options (allowedTools, permissionMode, maxTurns, systemPrompt) and it works in the filesystem. Fits embedding a code-editing agent in your own program.
  4. claude -p: the CLI form of the same Claude Code capabilities, for shell scripts and CI; the Agent SDK is its library form and the docs present them together. One-line rule: model call → API; filesystem agent → Agent SDK; quick scripted call → -p.
  5. Production nuance: with the Agent SDK you still own deployment (it supplies the harness, not hosting); auth is ANTHROPIC_API_KEY, and claude.ai subscription login can't be offered to third-party products. Follow-up: is the Agent SDK the same as the Messages API tool runner? No — the tool runner loops over tools you define and has no built-in file tools.

  分析过程 · 先想清楚再作答

  1. 这题考的是分层认知：三个入口底下是同一个模型，差别在于「谁提供循环和工具」。答成「SDK 更高级」没有信息量。
  2. Messages API（@anthropic-ai/sdk / anthropic）：一次请求一次响应，工具由你定义、循环由你写、上下文由你管。适合问答、抽取、分类、结构化输出、带引用的文档问答，以及你想完全掌控循环的自定义 Agent。
  3. Agent SDK（@anthropic-ai/claude-agent-sdk / claude-agent-sdk）：把 Claude Code 打包成库——内置 Read / Edit / Bash / Glob / Grep 等工具、完整的 agent 循环、上下文管理、权限系统、hooks、subagent、会话。你给一句任务和一组选项（allowedTools、permissionMode、maxTurns、systemPrompt），它在文件系统里干活。适合「在自己的程序里嵌一个会改代码的 agent」。
  4. claude -p：同一套 Claude Code 能力的命令行形态，适合 shell 脚本与 CI；Agent SDK 就是它的库形态，官方文档把两者放在同一页讲。判据一句话：要模型调用用 API，要文件系统里的 agent 用 Agent SDK，只想在脚本里调一下用 -p。
  5. 生产视角：Agent SDK 的部署仍是你自己的（它只提供循环，不提供托管），密钥走 ANTHROPIC_API_KEY，不能复用 claude.ai 的订阅登录给第三方产品。可预期的追问：Agent SDK 和 Messages API 里的 tool runner 是不是一回事？不是——tool runner 只帮你跑「你自己定义的工具」的循环，没有内置文件工具。

  Key points

  • Messages API: request/response, you write tools and the loop; for Q&A, extraction, structured output, custom agents
  • Agent SDK: Claude Code as a library with built-in file/Bash tools, loop, permissions, hooks; for embedding a code-editing agent
  • claude -p is the CLI form of the same capabilities, for scripts and CI
  • You still own deployment; auth via ANTHROPIC_API_KEY; the tool runner is not the Agent SDK

  答题要点

  • Messages API：一问一答，工具与循环自己写；适合问答、抽取、结构化输出、自定义 Agent
  • Agent SDK：Claude Code 的库形态，内置文件与 Bash 工具、循环、权限、hooks；适合嵌入会改代码的 agent
  • claude -p 是同一能力的命令行形态，适合脚本与 CI
  • 部署仍归自己，认证用 ANTHROPIC_API_KEY；tool runner 不是 Agent SDK