Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each

How to Implement Structured Output with Local LLMs

Why use it? How to implement it?

Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs

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 .

Your LLM Can Return Perfect JSON and Still Be Wrong

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 Fine-Tune an LLM: An End-to-End Guide

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.

llm 0.34

The release of llm 0.34 introduces a new feature that enhances log output by including response duration in both milliseconds and a human‑readable format. The short log view now contains a dedicated duration_ms field. Additionally, the update incorporates multiple bug fixes and a notable performance boost to llm logs, attributed to waveplate integration.

Your JSON Is Valid but Your Data Is Wrong: Five Failure Modes LLM Structured Outputs Won't Catch

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.

Introducing Structured Outputs in the API

We are introducing Structured Outputs in the API—model outputs now reliably adhere to developer-supplied JSON Schemas.

Building Multimodal Workflows with a Local LLM

Image inputs and structured outputs with Gemma 4 and Ollama The post Building Multimodal Workflows with a Local LLM appeared first on Towards Data Science .

SyGra: The One-Stop Framework for Building Data for LLMs and SLMs

Vision LLMs are PDF Parsers Too: Reading Charts and Diagrams for RAG

Enterprise Document Intelligence [Vol. 1 #5quater] - The other parsers read the words on a page.

Jev introduces a new shape of LLM - System One, aka Decision Models

TypeSafe AI’s new model, Jev, is a ‘System One’ or decision model that takes text or semi‑structured data as input and outputs floating‑point probabilities for yes/no, choice, or score questions, rather than text. It charges only for input tokens ($0.042/million) and offers free output, making it cheaper and faster than typical LLMs. The API lets users build a state object (string, array, or key‑value pairs) and query it with multiple questions, receiving confidence scores and probability distributions for each answer.

llm-openrouter 0.7

Release: llm-openrouter 0. 7 Now that this plugin is compatible with LLM 0.