Most LLM "structured output" tutorials are unserious. They teach you to ask for JSON politely and then hope the model behaves. That is not validation. That is optimism with braces. OpenAI's own docs make the distinction explicit. JSON mode gives you valid JSON, while Structured Outputs enforces schema adherence, and OpenAI recommends using Structured Outputs instead of JSON mode when possible. That still does not make the payload trustworthy. JSON Schema defines structure and allowed values, Pydantic gives you typed validation in Python, and OpenAI explicitly notes that a schema-valid response can still contain incorrect values. On top of that, refusals and incomplete outputs can bypass the shape you expected. In production, structured output validation is a pipeline, not a toggle. The same boundary also has to live inside the wider story of throughput, retries, and scheduler limits on the LLM performance engineering hub .…