Structured LLM Output Ensures Valid Syntax, Not Accurate or Trustworthy Data

Constrained decoding and JSON schema enforcement guarantee that AI model outputs are syntactically valid, but offer no assurance that the values are factually correct or semantically meaningful. Teams have increasingly conflated schema-valid output with correct output, creating a new class of production bugs where well-formed data contains wrong or hallucinated values. Unlike the malformed JSON errors of early LLM integrations, these failures produce no exceptions or logs, making them far harder to detect. The problem mirrors a lesson web developers learned with client-side form validation: passing a shape check does not mean the underlying data can be trusted. Developers are urged to treat structured LLM output like input from any untrusted API client, with schema validation as a starting point rather than a final safeguard.