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.
By Mostafa Ibrahim
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 .
By Maria Mouschoutzi
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 .
By Maria Mouschoutzi
Why use it? How to implement it?
By Shuai Guo
The article "How to Fine-Tune an LLM: An End-to-End Guide" offers a practical, hands‑on walkthrough for fine‑tuning large language models in real‑world scenarios. It covers the entire process from data preparation to deployment, providing readers with actionable steps to adapt LLMs to specific tasks. The guide is aimed at practitioners looking to implement fine‑tuning in a structured, end‑to‑end manner.
By Sam Black
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.
By Ibrahim Salami
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 .
By Kezhan Shi
The article titled "What We Miss About Missing Values" explores the often overlooked assumptions embedded in the data we observe, particularly focusing on how missing values can influence analysis and interpretation. It delves into the hidden biases and methodological implications that arise when data is incomplete, urging readers to consider these factors when working with real-world datasets.
By David Conneely
A hands-on guide to tracking experiments, logging models, and reproducing results with ML Flow. The post Are Your ML Experiments a Mess?
By Alex Davis
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 .
By Kezhan Shi
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.
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.
By Emmimal P Alexander