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AI Engineering/Phase 13/Lesson 04

Structured Output — JSON Schema, Pydantic, Zod, Constrained Decoding

"Ask the model nicely to return JSON" fails 5 to 15 percent of the time, even on frontier models. Structured outputs close that gap with constrained decoding: the model is literally prevented from emitting a token that would violate the schema. OpenAI's strict mode, Anthropic's schema-typed tool use, Gemini's `responseSchema`, Pydantic AI's `output_type`, and Zod's `.parse` are five surface forms of the same idea. This lesson builds the schema validator and the strict-mode contract learners will use for every production extraction pipeline.

Build 1.3 hr Python (stdlib, JSON Schema 2020-12 subset)

Prerequisites: Phase 13 · 02 (function calling deep dive)

#Learning Objectives

  • Write a JSON Schema 2020-12 for an extraction target using the right constraints (enum, min/max, required, pattern).
  • Explain why strict mode and constrained decoding give different guarantees from "validate after generation".
  • Distinguish the three failure modes: parse error, schema violation, model refusal.
  • Ship an extraction pipeline with typed repair and typed refusal handling.
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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.