Structured Output and Guardrails
Get clean JSON and reduce errors.
Structured Output and Guardrails is a free NLP Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the NLP Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
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: floatWhat 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. ✅
Frequently asked questions
Is the “Structured Output and Guardrails” lesson free?
Yes — the full text of “Structured Output and Guardrails” is free to read here on the web, and the NLP Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the NLP Academy course, upgrade to CoddyKit PRO.
What will I learn in “Structured Output and Guardrails”?
Get clean JSON and reduce errors. You practise NLP Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start NLP Academy?
No prior experience is required. NLP Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Structured Output and Guardrails” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this NLP Academy lesson?
Yes. Every NLP Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- What Makes a Model Large
- Calling an LLM From Python
- Zero-Shot and Few-Shot Prompting
- Structured Output and Guardrails