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structured output

40 articles · Page 1 of 1
Geek Labs
Geek Labs
Oct 23, 2026 · Artificial Intelligence

Jev Ultrafast: 25% Faster Browser Agents via Multiple-Choice Over Screenshots

The article details browser-use's Jev Ultrafast experiment, which replaces screenshot-based observation with a single-request multiple-choice model (Jev) to decide actions and target elements, reducing median task time from 9.45s to 7.09s (25%) and browser protocol calls from 1092 to 101, while adding safety validations and acknowledging limitations.

AI AgentsJev UltrafastTypeSafe Jev
0 likes · 14 min read
Jev Ultrafast: 25% Faster Browser Agents via Multiple-Choice Over Screenshots
Old Zhang's AI Learning
Old Zhang's AI Learning
Oct 19, 2026 · Artificial Intelligence

vLLM 0.32.0 Adds System 1 Decision Engine for Millisecond Structured Decisions

vLLM 0.32.0 introduces a built-in /v1/systemone endpoint that delivers millisecond-level structured decisions (choice, boolean, scoring) using models like LiquidAI/d1-3B, achieving 23ms median latency and 44 decisions per second, alongside KV cache quantization improvements, universal HF parsers, and breaking changes.

HF tool parserKV cache quantizationLLM inference
0 likes · 11 min read
vLLM 0.32.0 Adds System 1 Decision Engine for Millisecond Structured Decisions
Ops Development & AI Practice
Ops Development & AI Practice
Oct 18, 2026 · Artificial Intelligence

How Good Is macOS 27's Built-in `system` Model? A Reality Check

The article evaluates macOS 27's built-in `system` model accessed via the `fm` CLI, analyzing Apple's benchmarks, third-party tests, and actual capabilities like text generation, structured output, image input, and local API serving, concluding it's a useful local task component but not a flagship-level replacement, and `fm` cannot download additional models.

API serverApple Foundation Modelsbenchmarking
0 likes · 16 min read
How Good Is macOS 27's Built-in `system` Model? A Reality Check
Su San Talks Tech
Su San Talks Tech
Oct 18, 2026 · Artificial Intelligence

MinerU: Structure-Aware Document Parsing for RAG and Agent Workflows

This article explains why MinerU, an open-source document parser with 80k+ GitHub stars, outperforms traditional text-extraction tools by understanding document structure — handling multi-column layouts, tables, and formulas — and introduces its four-tier parsing modes, stable locators for Agent workflows, and practical integration code for Python and Java.

Agent workflowMinerUOpen Source
0 likes · 16 min read
MinerU: Structure-Aware Document Parsing for RAG and Agent Workflows
Architect
Architect
Sep 30, 2026 · Artificial Intelligence

Jev: Separating Semantic Judgment from Generation in LLM Applications

TypeSafe's Jev introduces a decision model that returns typed probability outputs (Noul, Choice, Score) instead of natural language, moving semantic judgment from runtime generation to a designable, testable 'decision surface' developed with LLM assistance but executed by a specialized model, enabling calibrated thresholds, parallel evaluation, and deterministic code control flow.

Agent EngineeringDecision SurfaceJev
0 likes · 26 min read
Jev: Separating Semantic Judgment from Generation in LLM Applications
Code Farmer Manor Chronicle
Code Farmer Manor Chronicle
Sep 28, 2026 · Artificial Intelligence

Prompt Engineering Fundamentals: Ask LLMs the Right Way

This guide covers essential prompt engineering techniques for large language models, including clear instructions, role assignment, output formatting, context injection, few-shot examples, chain-of-thought reasoning, structured outputs, troubleshooting common failures, and template-based prompting for RAG and agent applications.

AgentFew-shotLLM
0 likes · 7 min read
Prompt Engineering Fundamentals: Ask LLMs the Right Way
Linyb Geek Road
Linyb Geek Road
Sep 28, 2026 · Artificial Intelligence

Jev Decision Model: Extracting Structured Judgments from LLMs for Code-Driven Routing

This article details the Jev decision model from TypeSafe, which provides typed answers (Choice, Score, Noul) with probabilities instead of text, enabling code-driven routing, verification, and retrieval reranking with lower latency and cost than LLMs, while explaining its four architectural patterns, confidence gating, atomic decomposition, and limitations.

AI-powered softwareJevLLM verification
0 likes · 25 min read
Jev Decision Model: Extracting Structured Judgments from LLMs for Code-Driven Routing
PaperAgent
PaperAgent
Sep 22, 2026 · Artificial Intelligence

Jev: LLM Thinks, Jev Acts — 5 Demos Show 100x Cheaper, Faster AI Reflexes

The article introduces Jev, a fast, cheap AI model from TypeSafe that handles reflexive decisions while LLMs handle reasoning, showcasing five demos: context compression, ad analysis, real-time Mario gameplay, probability-based animations, and autonomous rocket landing — all at fractions of LLM cost and latency.

AI AgentsJevLLM
0 likes · 7 min read
Jev: LLM Thinks, Jev Acts — 5 Demos Show 100x Cheaper, Faster AI Reflexes
Data Bricklaying Diary
Data Bricklaying Diary
Sep 22, 2026 · Artificial Intelligence

Jev: Fast, Structured Judgments for AI Agents Without LLM Overhead

TypeSafe's Jev provides fast, constrained judgments (Choice, Score, Noul) for high-frequency agent decisions like ticket routing, model selection, and browser actions, cutting latency and cost while requiring rigorous evaluation, fallback, and human oversight because type safety does not guarantee correctness.

AI AgentsJevTypeSafe
0 likes · 14 min read
Jev: Fast, Structured Judgments for AI Agents Without LLM Overhead
ShiZhen AI
ShiZhen AI
Sep 20, 2026 · Artificial Intelligence

Jev: The AI Model That Decides, Doesn't Generate Text

Jev is a non-generative AI model that returns structured decisions (choices, scores, probabilities) instead of text, enabling high-speed, low-cost classification and routing tasks when problems are decomposed into bounded micro-decisions with confidence thresholds.

ClassificationJevTypeSafe AI
0 likes · 16 min read
Jev: The AI Model That Decides, Doesn't Generate Text
AndroidPub
AndroidPub
Sep 20, 2026 · Artificial Intelligence

Decision Models: Why AI Should Judge, Not Just Generate

The article explains how decision-making AI models like Jev output structured judgments (choices, scores, booleans) instead of natural language, making them better suited for high-frequency, low-risk business decision nodes such as ticket routing, content moderation, and agent workflow control, and discusses deployment strategies like shadow testing and confidence calibration.

AI architectureJevbusiness automation
0 likes · 14 min read
Decision Models: Why AI Should Judge, Not Just Generate
James' Growth Diary
James' Growth Diary
Sep 13, 2026 · Artificial Intelligence

CLI for AI Agents: Scenario Domains, Skills & Orchestratable Output

The article explains how to design CLIs for AI hosts like Cursor by organizing commands into lifecycle-based scenario domains, binding Skills that define trigger boundaries and workflows, and emitting structured JSON with next_command for orchestration, avoiding pitfalls like resource-path mirroring, verbose output, and missing workspace context.

AI AgentsAI host integrationCLI design
0 likes · 36 min read
CLI for AI Agents: Scenario Domains, Skills & Orchestratable Output
Chengwu Tech Stack
Chengwu Tech Stack
Sep 6, 2026 · Artificial Intelligence

Building an Explainable Person-Detection Contract for Video Frame Sampling

This tutorial defines a three-state judgment contract (present, not_observed, uncertain) for detecting real persons in sampled video frames, covering prompt design with structured JSON output, evidence requirements, code-level aggregation rules, and evaluation methodology using boundary-case samples.

AI visionevaluation methodologyjudgment contract
0 likes · 15 min read
Building an Explainable Person-Detection Contract for Video Frame Sampling
Qborfy AI
Qborfy AI
Jun 27, 2026 · Artificial Intelligence

Advanced Guide to LLM API: Multimodal Input and Structured Output

This advanced tutorial explores how LLM APIs handle multimodal inputs and produce structured outputs, detailing format differences, image‑generation parameters, JSON vs JSON‑Schema responses, platform‑specific quirks, practical code examples, and best‑practice strategies for building reliable production pipelines.

APIJSON SchemaLLM
0 likes · 19 min read
Advanced Guide to LLM API: Multimodal Input and Structured Output
LuTiao Programming
LuTiao Programming
Jun 23, 2026 · Artificial Intelligence

Spring AI 2.0’s New Lifesaver: Guaranteed JSON Output from Large Models

Spring AI 2.0 adds self‑healing structured output with schema validation and provider‑side constraints, letting Java applications receive reliable JSON objects from large language models, eliminating brittle string‑cleaning code while still requiring business‑level validation.

AI IntegrationJSON SchemaJava
0 likes · 20 min read
Spring AI 2.0’s New Lifesaver: Guaranteed JSON Output from Large Models
Coder Trainee
Coder Trainee
Jun 19, 2026 · Artificial Intelligence

Deep Dive into Spring AI: Advanced ChatClient, Prompt Templates, and Function Calling

This article explores Spring AI's core design patterns, advanced ChatClient usage, dynamic PromptTemplate creation, few‑shot prompting, structured output parsing, and declarative function calling with @Tool annotations, providing code examples, advisor mechanisms, and testing tips for Java developers.

AI IntegrationChatClientFunction Calling
0 likes · 13 min read
Deep Dive into Spring AI: Advanced ChatClient, Prompt Templates, and Function Calling
DeepHub IMBA
DeepHub IMBA
Jun 16, 2026 · Artificial Intelligence

10 Essential LangChain & LangGraph Concepts Every AI Engineer Must Master

The article outlines ten core concepts—State, Node, Chain vs Graph, Routing, Retrieval, Structured Output, Streaming, Memory, Checkpointing, and Human‑in‑the‑Loop—explaining why they are crucial for building reliable, scalable AI agents and showing concrete Python examples for each.

AI AgentsLangChainLangGraph
0 likes · 11 min read
10 Essential LangChain & LangGraph Concepts Every AI Engineer Must Master
Tech Ocean
Tech Ocean
Jun 6, 2026 · Artificial Intelligence

Spring AI Day 4: Get LLMs to Return Java Objects Directly, No Manual Parsing

The article explains how Spring AI’s .entity() method lets developers obtain structured Java objects such as POJOs, lists, enums, and response entities directly from LLM outputs, eliminating the need for manual JSON parsing and handling generic‑type issues with ParameterizedTypeReference.

EntityJavaLLM
0 likes · 5 min read
Spring AI Day 4: Get LLMs to Return Java Objects Directly, No Manual Parsing
The Dominant Programmer
The Dominant Programmer
May 25, 2026 · Artificial Intelligence

Mastering Structured Output in Spring AI: Getting Precise JSON from Large Language Models

This article walks through using Spring AI with Ollama to enforce JSON‑schema‑based structured output for agents, showing why structured responses matter, how Spring AI generates schemas from Java beans, and providing complete runnable code for both basic and advanced tool‑calling scenarios.

AgentFunction CallingJSON Schema
0 likes · 11 min read
Mastering Structured Output in Spring AI: Getting Precise JSON from Large Language Models
Su San Talks Tech
Su San Talks Tech
May 18, 2026 · Artificial Intelligence

How to Guarantee Reliable Function Calling in LLM Agents

The article breaks down the reliability challenges of LLM Function Calling, categorizes five failure modes, and presents concrete engineering safeguards such as precise schema design, tool description, constraint enforcement, few‑shot calibration, structured output, validation‑feedback loops, monitoring, and risk‑aware trade‑offs.

Function CallingJSON SchemaLLM
0 likes · 17 min read
How to Guarantee Reliable Function Calling in LLM Agents
MeowKitty Programming
MeowKitty Programming
Apr 21, 2026 · Backend Development

2026 AI Priorities for Java Developers: Structured Output, RAG, and Observability

While many Java teams chase flashy AI demos and agents, the real 2026 focus has shifted to engineering concerns—ensuring model outputs reliably map to Java objects, integrating Retrieval‑Augmented Generation into robust data pipelines, and adding observability so AI services can be monitored and debugged like traditional back‑end components.

AIlangchain4jobservability
0 likes · 7 min read
2026 AI Priorities for Java Developers: Structured Output, RAG, and Observability
JavaGuide
JavaGuide
Apr 14, 2026 · Artificial Intelligence

Interview Question: How to Build Prompt Engineering for an Agent and Defend Against Malicious Prompt Injection

The article explains how industrial‑grade AI agents require structured prompt engineering, chain‑of‑thought reasoning, task decomposition, and a three‑layer defense (sandbox, prompt isolation, and human approval) to prevent prompt‑injection attacks, while also covering context engineering, retrieval‑augmented generation, and tool design best practices.

Context EngineeringLLM securityagent design
0 likes · 23 min read
Interview Question: How to Build Prompt Engineering for an Agent and Defend Against Malicious Prompt Injection
James' Growth Diary
James' Growth Diary
Apr 7, 2026 · Artificial Intelligence

Parser vs withStructuredOutput: Choosing the Right Structured Output for LangChain

The article analyzes why LLMs often return unstructured text, compares LangChain's OutputParser and withStructuredOutput approaches, evaluates their stability, token usage, and model compatibility, and provides a decision guide and best‑practice recommendations for production‑grade structured output in 2025.

Function CallingLLMLangChain
0 likes · 10 min read
Parser vs withStructuredOutput: Choosing the Right Structured Output for LangChain
AI Tech Publishing
AI Tech Publishing
Feb 25, 2026 · Artificial Intelligence

How to Build a Code Review Agent from Scratch Using Claude Agent SDK (Part 1)

This tutorial walks through creating a full‑featured code‑review Agent with Claude Agent SDK, covering installation, TypeScript setup, the SDK‑managed agent loop, structured JSON output, permission handling, sub‑agents, session management, hooks, custom MCP tools, cost tracking, and a production‑grade example.

AI AgentClaude Agent SDKTypeScript
0 likes · 21 min read
How to Build a Code Review Agent from Scratch Using Claude Agent SDK (Part 1)
DeepNoMind
DeepNoMind
Feb 14, 2026 · Artificial Intelligence

Hierarchical Agent Teams: Boosting Reliability in Agentic AI

This article presents the hierarchical agent‑group design pattern for reliable agentic AI, explains how specialized orchestrator and executor agents exchange structured Pydantic data, demonstrates a LangGraph workflow, and shows that the hierarchical approach yields faster execution (13.57 s vs 18.34 s) and higher‑quality reports compared with a monolithic agent.

Agentic AILangGraphhierarchical agents
0 likes · 15 min read
Hierarchical Agent Teams: Boosting Reliability in Agentic AI
Data STUDIO
Data STUDIO
Feb 12, 2026 · Artificial Intelligence

How to Add Tools to a LangGraph AI Agent for Real‑World Tasks

This tutorial walks through adding custom, pre‑built, and server‑side tools to a LangGraph AI agent, demonstrates a ReAct workflow, implements conditional edges for web search, enforces structured output for intelligent shutdown, and shows how to monitor token usage with callbacks, all with runnable Python code.

AI AgentLangGraphPython
0 likes · 16 min read
How to Add Tools to a LangGraph AI Agent for Real‑World Tasks
Fun with Large Models
Fun with Large Models
Nov 8, 2025 · Artificial Intelligence

Unlocking LangChain 1.0 create_agent: Advanced MCP, Structured Output, Memory & Middleware

This guide dives into the four advanced capabilities of LangChain 1.0's create_agent API—MCP tool integration, structured output, memory management, and middleware—showcasing practical examples such as an Amap MCP planner, Pydantic‑based response formatting, InMemorySaver chat history, and custom middleware for dynamic model selection.

AI AgentsLangChainMCP
0 likes · 22 min read
Unlocking LangChain 1.0 create_agent: Advanced MCP, Structured Output, Memory & Middleware
Fun with Large Models
Fun with Large Models
Nov 2, 2025 · Artificial Intelligence

Fast-Track LangChain 1.0: Core Upgrades and the New create_agent API

This guide walks through LangChain 1.0’s three major upgrades— the new create_agent API that replaces legacy agent builders, standardized content_blocks for unified model output, and a streamlined package structure—while showing how middleware hooks, built‑in and custom middleware, and improved structured output simplify production‑grade AI agent development.

AI AgentsLangChainPython
0 likes · 15 min read
Fast-Track LangChain 1.0: Core Upgrades and the New create_agent API
Code Archaeology
Code Archaeology
Oct 27, 2025 · Artificial Intelligence

Understanding LLM Output & Sampling: Temperature, Top-k, Top-p with DeepSeek Examples

This article explains how LLMs generate tokens via probability distributions, details three sampling strategies — temperature, top-k, and top-p — with concrete DeepSeek API examples, outlines four methods for structured output, and discusses why hallucination is an inherent feature of probabilistic generation rather than a bug.

DeepSeekLLMhallucination
0 likes · 8 min read
Understanding LLM Output & Sampling: Temperature, Top-k, Top-p with DeepSeek Examples
Alibaba Cloud Developer
Alibaba Cloud Developer
Oct 15, 2025 · Artificial Intelligence

Mastering Structured Output in Large Language Models: Techniques, Challenges, and Future Trends

Large language models are evolving from free‑form text generators to reliable data providers by mastering structured output through prompt engineering, validation frameworks, constrained decoding, supervised fine‑tuning, reinforcement learning, and API‑level capabilities, enabling seamless integration with software systems while addressing hallucinations and format reliability.

APILLMconstrained decoding
0 likes · 28 min read
Mastering Structured Output in Large Language Models: Techniques, Challenges, and Future Trends
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 21, 2025 · Artificial Intelligence

How Browser‑Use Leverages AI Prompts for Seamless Browser Automation

This article explains how the open‑source browser‑use framework combines carefully designed SystemMessage prompts, structured HumanMessage inputs, and LangChain‑driven tool calls to enable large language models to automate complex web tasks such as shopping, CRM updates, résumé processing, and document generation, while providing concrete code examples and best‑practice tips.

AI automationLangChainbrowser automation
0 likes · 21 min read
How Browser‑Use Leverages AI Prompts for Seamless Browser Automation
AI Large Model Application Practice
AI Large Model Application Practice
Feb 17, 2025 · Artificial Intelligence

Mastering Structured Output for DeepSeek‑R1 with LangChain, LangGraph, and ReAct Agents

DeepSeek‑R1 excels at deep reasoning but lacks native structured output; this guide explains why structured output matters, outlines common API‑level techniques, and provides three practical solutions—using an auxiliary model with a LangChain chain, a LangGraph workflow, and a ReAct agent—complete with code snippets and JSON‑mode tips.

DeepSeekLLMLangChain
0 likes · 12 min read
Mastering Structured Output for DeepSeek‑R1 with LangChain, LangGraph, and ReAct Agents
Ma Wei Says
Ma Wei Says
Feb 13, 2025 · Artificial Intelligence

Master AI Prompting: 5 Proven Techniques to Unlock Accurate Outputs

This guide presents five practical prompting techniques—including structured output, role‑playing, visual conversion, multi‑turn refinement, and multilingual handling—plus industry‑specific examples and common pitfalls, helping users craft precise commands for AI models like DeepSeek.

AI promptingLarge Language Modelsprompt engineering
0 likes · 8 min read
Master AI Prompting: 5 Proven Techniques to Unlock Accurate Outputs
Java Architecture Diary
Java Architecture Diary
Jan 10, 2025 · Artificial Intelligence

Generate Structured JSON with Ollama LLM Using Java

This guide explains why structured JSON output from LLMs is essential, walks through installing and running Ollama, and provides a complete Java Spring Boot implementation—including POJOs, service code, and best‑practice tips—to retrieve AI‑generated data in a reliable, parsable format.

AIJSONLLM
0 likes · 7 min read
Generate Structured JSON with Ollama LLM Using Java