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结构化输出与防护机制

获取整洁的 JSON 并减少错误

结构化输出与防护机制 是 CoddyKit 上的免费 NLP Academy 课时。 这是第 4 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 NLP Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 NLP Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

Free Text Is Hard to Use

A chatty paragraph is nice for humans but painful for code. To build apps, you want structured output you can parse reliably. 🧱

Ask for JSON

The simplest fix is to ask the model to reply as JSON, naming the exact fields you expect in the answer.

prompt = "Return JSON with keys sentiment and score for: I loved it!"

Define the Shape

Be precise about the schema: list every key, its type, and allowed values. Ambiguity is what makes parsing break later.

JSON Mode

Many APIs offer a JSON mode that forces valid JSON, so the model cannot wrap the answer in chatty prose.

response_format={"type": "json_object"}

Parse Safely

Always wrap parsing in try/except. Even good models occasionally slip, and your code should fail gracefully when they do.

import json
data = json.loads(text)

Validate the Result

Parsing is not enough. Validate that required keys exist and values are in range before you trust the data downstream.

Schemas With Pydantic

A Pydantic model defines your fields once and validates the parsed output automatically, raising a clear error on bad data.

from pydantic import BaseModel
class Result(BaseModel):
    sentiment: str
    score: float

What Guardrails Are

Guardrails are the checks around a model that keep it on track: validation, retries, and limits on what it may output.

Retry on Bad Output

If validation fails, send the error back and ask the model to fix its output. One retry resolves most small mistakes.

Constrain the Choices

For category tasks, tell the model the exact allowed labels. A closed list stops it from inventing new categories.

Guard Against Hallucination

Ask the model to say it does not know rather than guess. A clear fallback beats a confident but wrong answer.

Quick Check

What should you do right after parsing an LLM's JSON output?

Recap

Ask for JSON, define a schema, parse safely, then validate. Add guardrails like retries and closed label lists to stay reliable. ✅

常见问题解答

「结构化输出与防护机制」课时是免费的吗？

是的 — 「结构化输出与防护机制」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 NLP Academy 课程的其余内容，请升级到 CoddyKit PRO。 NLP Academy 课程共包含 4 节课。

「结构化输出与防护机制」这节课中我会学到什么？

获取整洁的 JSON 并减少错误 你通过在浏览器中直接运行的动手代码来练习 NLP Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 NLP Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 NLP Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 4 节课，共 4 节。

「结构化输出与防护机制」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 NLP Academy 课中编写并运行代码吗？

能。每节 NLP Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

  1. 模型为何会变大
  2. 从 Python 调用 LLM
  3. 零样本与少样本提示
  4. 结构化输出与防护机制
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