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While native structured output enforcement eliminates malformed JSON, it can silently introduce hallucinated values when source data lacks specific fields like dates. To prevent downstream failures, schemas must define optional fields for missing information rather than forcing the model to invent plausible but incorrect data. This distinction between extraction and inference is critical for maintaining data integrity in production pipelines.
Your LLM Can Return Perfect JSON and Still Be Wrong
What I learned after thinking more carefully about Structured Outputs on messy, incomplete data The post Your LLM Can Return Perfect JSON and Still Be Wrong appeared first on Towards Data Science.
Three weeks after I turned on Structured Outputs for a pipeline that parsed payment confirmation messages into transaction records, I noticed that our reconciliation job started flagging a small, steady stream of mismatches. They were not crashes, and not malformed rows either. Just transactions where the amount and sender matched perfectly but the date was off. Something like 2 to 3% of a given week's volume, enough to notice, not enough to be obvious right away. At first I assumed that it was a timezone bug. But it wasn't. When I pulled the raw source messages next to the extracted records, a pattern showed up: every mismatched transaction came from a message that never mentioned a date at all. Something like "Payment received from Chinedu, ₦45,000, ref TXN-82K91." No date anywhere in the text. And the…
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