The article discusses five failure modes that can slip through constrained decoding in large language models, explaining why these errors are not detected by schema validators. It highlights that even when JSON output is syntactically valid, the underlying data can still be incorrect. The post serves as a warning that relying solely on schema validation is insufficient for ensuring correct structured outputs from LLMs.
The article discusses insights gained from a deeper examination of Structured Outputs when dealing with messy, incomplete data. It highlights that even when a large language model returns perfectly formatted JSON, the content can still be incorrect. The author reflects on the implications of this observation for data science practices.
How to stop parsing JSON by hand and start trusting your model's output The post Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs appeared first on Towards Data Science .
Getting reliable, readable responses out of your LLM, and knowing which tool to reach for The post Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each appeared first on Towards Data Science .
LLM rate limits don't just interrupt agent pipelines—they can silently corrupt structured outputs when fallback models receive incompatible payloads. I built a recovery layer that classifies failures, adapts payloads across model tiers, preserves execution state, and maintains schema integrity during provider swaps.
Why use it? How to implement it?
Enterprise Document Intelligence [Vol. 1 #8C] - Structured output is the start of validation, not the end: check the evidence, accept not-found, loop the feedback The post Validating the RAG Answer Before the User Sees It: Spans, Quotes, and the Feedback Loop appeared first on Towards Data Science .
Enterprise Document Intelligence [Vol. 1 #8A] - The schema is the contract: every field is a question the pipeline asks the model, and every answer is checkable The post Stop Returning Text from RAG: The Typed Answer Contract That Prevents Hallucination appeared first on Towards Data Science .
The article describes how to deploy a trained churn classifier as a FastAPI service so that other software can call it. It focuses on the practical steps needed to transform a model that performs well in isolation into a usable, callable API. The post is aimed at readers who want to make their machine‑learning models accessible in real-world applications.
We are introducing Structured Outputs in the API—model outputs now reliably adhere to developer-supplied JSON Schemas.
arXiv:2607. 18261v1 Announce Type: new Abstract: LLM agents are increasingly used as transaction compilers: a user states an intent in natural language, and the model emits a structured object that an API can execute.
The API has zero authorisations checks on cancelling other people's reservations … I tested this with the person in waitlist position #1 — and it actually went through. So you've moved from #4 to #3 already.
Enterprise Document Intelligence [Vol. 1 #8quater] - Two angles on the cascade, cost and a validation loop, backed by a real sweep of twenty local models against a hosted flagship The post Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship appeared first on Towards Data Science .