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On September 1, 2026, the article published by Towards Data Science pointed out that large language models (LLMs) have five common failure modes when generating structured outputs. Although the generated JSON format may pass syntax validation, the internal data content can still contain serious errors. The article analyzed the causes and manifestations of these failures, emphasized the issue that relying solely on format checks cannot guarantee data accuracy, and reminded developers to design more comprehensive verification mechanisms in conjunction with business logic.
The article “Your JSON Is Valid but Your Data Is Wrong: Five Failure Modes LLM Structured Outputs Won’t Catch” identifies five failure modes that cannot be captured by constrained decoding and their causes. Published by Towards Data Science, this article reveals five situations where the output complies with JSON Schema validation, but the data content may still be incorrect.