#JSON Schema generation
375 related articles
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ui-skills: An AI Skills Library Built for Design Engineers
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DeepAgents in Practice: A Complete Guide to Building a HarmonyOS Automated Testing Agent
A deep dive into DeepAgents' core mechanisms, with a hands-on guide to building a HarmonyOS automated testing Agent — covering create_deep_agent, LangChain comparison, and long-chain task planning.
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AI Testing in a Closed Loop: Full Pipeline from API Collection and Test Case Generation to Agent-Driven Execution
Full breakdown of a real AI testing pipeline: API collection, doc enrichment, AI test case generation, Agent-driven execution, and test reports — with Skills, RAG, and Harness engineering.
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BAML: A Type-Safe Programming Language…
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BAML: A Type-Safe Programming Language Designed for AI Agents
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Claude Code Enterprise Guide: Environment Setup, Git Workflows, and Switching to Chinese AI Models
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Forge: Adding Reliability Guardrails to Local Model Tool Calling
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Designing APIs for AI Agents: A Paradi…
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Designing APIs for AI Agents: A Paradigm Shift from Human Interfaces to Agent-Friendly Design
When AI Agents become the primary API callers, traditional interface design assumptions break down. This article explores agent-friendly API design principles and how MCP is driving this paradigm shift.
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Chrome Built-in AI in Action: Six APIs for Building a Smart Blog Editor
Deep dive into Chrome Built-in AI's six core APIs — Summarizer, Prompt API, Translator and more — applied to a real blog editor. Local, private, no server needed.
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LangChain + LangGraph Full-Stack in Practice: A Production-Ready Guide to Six Core Modules and Agent Deployment
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Claude Opus 4 + RAG + Knowledge Graph: A Real-World Architecture for AI Novel Writing
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Adversarial Testing of AI Agents in the Terminal via MCP
Learn how to use MCP (Model Context Protocol) to run adversarial tests on AI agents in the terminal, covering prompt injection, privilege escalation, and dangerous command execution scenarios.
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StyleSeed: A Design Rules Engine to En…
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StyleSeed: A Design Rules Engine to End the Homogenization Problem in AI-Generated UI
StyleSeed is an open-source design-rules engine that injects structured color, typography, and component specs into AI workflows to eliminate generic UI output.
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How DSLs Make LLM Outputs More Reliabl…
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How DSLs Make LLM Outputs More Reliable: Principles and Practice
LLM output instability is a core production challenge. This article analyzes how DSLs improve LLM reliability through verifiability, semantic convergence, and structural constraints.
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Agentic Loop Explained: The Three-Loop…
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Agentic Loop Explained: The Three-Loop Architecture of Reasoning, Tool Use, and Orchestration
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n8n Automated AI Video Generation Pipeline: From Prompts to Batch Output — Complete Guide
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Quantifying LLM Output Quality with Pydantic Evals and Integrating It into CI Quality Gates
Learn how to build an LLM evaluation system with Pydantic AI and Amazon Bedrock — turning fuzzy AI output quality into automated, CI-enforceable engineering metrics.
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Six Layers of Defense: Engineering Reliable Structured Output from LLMs
LLM JSON output unstable in your Agent? This guide covers 6 engineering layers: constrained decoding, validation retry, fake tool calls, Logit Masking, Schema contracts, and anti-pattern locking.
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Complete Guide to LangChain 1.3: From Models and Agents to DeepAgent Architecture
A complete guide to LangChain 1.3: LLM invocation, Agent tool calling, Harness architecture, LangGraph, RAG, and DeepAgent — build a clear, modern Agent development knowledge base.
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GPT-2 Fine-Tuning Experiment: 88% Func…
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GPT-2 Fine-Tuning Experiment: 88% Function Calling Success Rate with a 355M Small Model
A developer fine-tuned GPT-2 (355M) on free Kaggle GPUs and achieved 88% function calling success. Here's what this counter-intuitive experiment reveals about small models and LLM agent capabilities.
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AI Agent Development in Practice: A Complete Guide to Framework Selection, Tool Calling, and Production Deployment
A comprehensive guide to AI Agent development: covering Agent vs. Chatbot differences, framework selection, tool calling design, RAG pipeline setup, and production deployment best practices.
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