Introduction to Structured Output Beginner

AI models generate free-form text by default. But production applications need structured, machine-readable data - JSON objects, typed fields, validated schemas. Structured output techniques bridge this gap, turning unreliable text into data you can trust.

The Problem

Without structured output, you face these challenges:

# You ask: "Extract the name, age, and city from this text"

# Sometimes you get:
{"name": "Alice", "age": 30, "city": "NYC"}      # Perfect!

# Other times you get:
Here's the extracted data:
- Name: Alice
- Age: 30
- City: New York City                              # Not JSON!

# Or even:
```json
{"name": "Alice", "age": "thirty", "city": "NYC"} # Wrong type!
```

Why Structured Output Matters

Approaches Overview

What We Will Cover

• Lesson 2 - JSON Mode: Provider-native JSON guarantees from OpenAI, Anthropic, and Google
• Lesson 3 - Pydantic Output: Type-safe structured output with automatic validation
• Lesson 4 - XML Output: When and how to use XML for structured responses
• Lesson 5 - Validation: Building robust validation pipelines with retries and fallbacks
• Lesson 6 - Best Practices: Production patterns and common pitfalls