Thoughtworks Technology Radar Vol 34 (April 2026) puts Structured output from LLMs in the Adopt ring. That's the "industry should be doing this" tier — not "evaluate," not "trial." This post is a 500-line vanilla JS tool that makes the technique concrete: paste a JSON Schema, see the natural-language prompt fragment an LLM consumes, see the synthesised example output, and validate whatever the model returns against the original schema. No LLM API calls, no build step.
Structured output is two halves
"Give the LLM a schema" is one sentence, but in code it's two responsibilities:
Schema → prompt. LLMs don't read JSON Schema syntax directly. type, properties, required get re-expressed as natural-language bullets the model can follow. OpenAI's response_format: { type: "json_object" } API lets you pass a schema, but the same translation happens server-side.
Output → validation. Even with structured output, models hallucinate enum values, miss required fields, blow past maximum. Validating at the boundary turns runtime drift into a typed error you can handle.
