#JSON Schema
500 related articles
·3 min
Introducing LLM Gateways in Production: Five Key Concerns and the Prerequisites for Trust
Introducing an LLM Gateway in LangChain production brings unified APIs and auto-fallback, but also quality drift, cost spikes, and debug black boxes. This article breaks down the five key engineering concerns and what it takes to earn trust.
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·4 min
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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·4 min
LangChain in Practice: Mastering the Four Core Modules of the Agent Ecosystem
A deep dive into LangChain's four core modules: LangChain components, LangGraph orchestration, Deep Agents, and LangSmith. Build your first Agent from scratch.
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·3 min
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…
·2 min
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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Deep Dives
Deep Dive into AI Agent Skill Design: …
·3 min
Deep Dive into AI Agent Skill Design: Engineering Practices from Anthropic and Perplexity
A deep dive into Skill design philosophy from Anthropic's Claude Code team and Perplexity's Agent team, covering the Tax Test, Gotchas Flywheel, progressive disclosure, and Eval-First practices for building high-quality AI Agent skill systems.
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·2 min
MCP vs. Skills: A Clear Breakdown of AI Agent Architecture Layers
MCP and Skills aren't alternatives — they occupy different layers of AI Agent architecture. This article breaks down Function Call, MCP, and Skills to clarify each layer's role.
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Voice-Driven Geometric Interaction: LL…
·2 min
Voice-Driven Geometric Interaction: LLM Semantic Parsing and Function Calling Architecture in Practice
How can LLMs translate natural language into geometric operations? This article breaks down a voice-driven geometry system: LLM for semantic parsing, Function Calling to bridge the geometry SDK.
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·2 min
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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·2 min
QuantaMind: An Open-Source Tool for Testing AI Agent Reliability Locally
QuantaMind is a free, open-source local AI Agent reliability testing tool using pass^k scoring and deterministic evaluation, supporting Ollama, llama.cpp, vLLM, and more.
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·3 min
Programmer to AI Engineer: You're Already 80% There — Here Are the 6 Skills You're Missing
Programmers transitioning to AI engineering aren't starting from scratch. Learn the 6 core skills — LLM APIs, RAG, prompt engineering, LLMOps — needed to make the leap.
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·2 min
From Beginner to Advanced AI Agent Development: A Deep Dive into One Essential Hands-On Book
A deep dive into a hands-on AI Agent development book covering component architecture, RAG, multi-agent systems, Function Calling, and production observability.
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Structured Information Extraction with…
·3 min
Structured Information Extraction with Local 7B Quantized Models: Root Cause Analysis and Engineering Optimization
Using Qwen 2.5 7B quantized locally to extract 60+ fields from insurance/financial contracts? Learn why it struggles and how task splitting, RAG, GBNF, and smarter chunking can fix it.
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sqlite-utils 4.1.1: Fix for Silent Dat…
·2 min
sqlite-utils 4.1.1: Fix for Silent Data Loss Risk in Foreign Key Transactions
sqlite-utils 4.1.1 fixes a silent data loss risk in table.transform() caused by foreign key cascade actions inside transactions, introducing a TransactionError fail-fast mechanism.
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The Hidden Costs of AI Coding Tools: A…
·2 min
The Hidden Costs of AI Coding Tools: A Deep Dive into Claude Code's 33k Token Overhead
Claude Code consumes up to 33,000 tokens per request before reading your prompt, vs. 7,000 for OpenCode. We break down why this 5x gap matters for cost, latency, and context windows.
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Migrating a Production AI Agent to GPT…
·2 min
Migrating a Production AI Agent to GPT-5.6: 2.2x Faster, 27% Cheaper — Engineering Lessons Learned
A production AI Agent migrated to GPT-5.6 achieved 2.2x faster speed and 27% lower cost. Deep dive into prompt compatibility, eval frameworks, and migration best practices.
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·4 min
MCP Protocol Deep Dive: Its Relationship with Function Calling and Agents, Plus a Practical Guide
Deep dive into MCP (Model Context Protocol): clarifying the three-layer relationship between MCP, Function Calling, and Agents, covering protocol roles, call flows, transport choices, and production security for AI developers.
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·4 min
OpenAI Plugins Deep Dive: The Plugin System That Connects ChatGPT to the Real World
A deep dive into OpenAI Plugins: how the system works, its technical architecture, and why it matters. Learn how ChatGPT uses plugins to access real-time data, take real-world actions, and lay the groundwork for AI Agents and Tool Use.
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·4 min
A Deep Dive into Claude Cookbooks: A Guide to the Official 47K-Star Hands-On Code Repository
Anthropic's open-source Claude Cookbooks project offers runnable Jupyter Notebook examples covering RAG, Tool Use, multimodal processing, and more—helping developers master Claude API best practices.
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·5 min
How Can Working IT Professionals Learn AI Efficiently? A Practical Learning Path from Zero to Real-World Projects
Limited time but want to learn AI systematically? This guide maps out a practical learning path for working IT pros—from AI application engineering and prompt engineering to RAG and Agents.
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