面试题库

Claude 高效使用：从对话到 Claude Code

D1 提示词进阶与 Claude 的「性格」：system prompt、XML 标签、让模型先思考、结构化输出

• 什么时候该让模型先写分析再回答，什么时候直接要结构化输出？两者能同时要吗？When should you have the model write its analysis before answering, and when should you go straight to structured output? Can you have both?

  国内高频海外高频进阶#structured-output#reasoning

  分析过程 · 先想清楚再作答

  1. 这题考取舍，判据是「谁消费输出」和「错误的代价」。答「都用」或「看情况」没有信息量，要给出可执行的判断句。
  2. 推导链：分析段是输出 token，按输出价计费，且会让响应变长；它换来的是复杂任务上更高的准确率与可核对的推理过程。所以任务越复杂、错误代价越高、越需要人审计，越该要分析段；批量、简单、程序直接消费的任务，直接要结构化输出。
  3. 「能不能同时要」：一旦传了 JSON Schema，输出被约束成 JSON，自由文本的分析段没地方放。两条路：在 schema 里加一个 reasoning 字段放在其他字段前面（模型会先生成它），或者依赖模型内部的 thinking——它是内部推理，你控制深度不控制内容。
  4. 生产视角：结构化输出解决的是「解析可靠性」，不是「判断正确性」；schema 不支持数值范围与字符串长度约束，这些校验要自己补。
  5. 可预期的追问：分析段会不会被程序误用？会，所以要用标签把分析段与答案段分开，程序只取答案段；另一个追问是 thinking 与分析段的区别——一个内部一个外显，一个控深度一个控内容。

  How to reason about it · think before answering

  1. This is a trade-off question; the criteria are who consumes the output and what an error costs. Give a decision rule, not 'it depends'.
  2. Chain: the analysis is output tokens, billed at output rates and adding latency, in exchange for higher accuracy on complex tasks and an auditable trace. The more complex, high-stakes, or human-reviewed the task, the more you want it; bulk, simple, machine-consumed tasks go straight to structured output.
  3. Can you have both? Once a JSON schema is passed the output is constrained to JSON, so free-text analysis has nowhere to go. Two options: add a reasoning field placed before the other fields, or rely on the model's internal thinking, whose depth you control but not its content.
  4. Production nuance: structured output fixes parsing reliability, not judgment quality; the schema cannot express numeric ranges or string lengths, so validate those yourself.
  5. Follow-ups: can the analysis leak into downstream code? Yes — separate analysis and answer with tags and parse only the answer. Thinking versus a written analysis: internal versus visible, depth versus content.

  答题要点

  • 分析段：输出 token 计费、更慢，但复杂任务更准、过程可核对；适合高风险、需人审的任务
  • 结构化输出：程序直接消费、解析零失败；适合批量、简单、明确的抽取与分类
  • 同时要：在 schema 里加靠前的 reasoning 字段，或依赖内部 thinking
  • 结构化输出保证的是格式不是正确性；范围与长度校验要自己补

  Key points

  • Written analysis costs output tokens and latency but raises accuracy and gives an auditable trace — use for high-stakes, human-reviewed work
  • Structured output is consumed directly by code with zero parse failures — use for bulk, simple extraction and classification
  • To combine: put a reasoning field first in the schema, or rely on internal thinking
  • Structured output guarantees shape, not correctness; add range and length validation yourself