#query decomposition
209 related articles
·3 min
Getting Started with LangChain 1.3: Building Intelligent Agent Applications with Deep Agent
Learn LangChain 1.3 core concepts including LLM model abstraction, RAG retrieval-augmented generation, and Agent orchestration. Build a Deep Agent with planners, tools, and reflection modules.
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·2 min
AI Agent Beginner's Guide: A Structured Learning Path and Hands-On Project Roadmap
New to AI Agents? This guide breaks down the full learning path — covering Agent principles, Prompt Engineering, RAG, multi-Agent systems, and hands-on projects to get you building fast.
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·3 min
How to Choose an AI Agent Development Course? 5 Practical Screening Criteria to Avoid Common Pitfalls
How to choose a quality AI Agent development course? This guide covers 5 key criteria: complete delivery pipeline, resume-worthy projects, real engineering perspective, update frequency, and mentorship.
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·3 min
New Retrieval Paradigms: Hobbit's Hard Negative Construction and Disco's Collaborative Coverage Index Design
A deep dive into two frontier dense retrieval works: Hobbit uses gradient analysis to automatically construct hard batches; Disco replaces single-document competition with submodular collaborative coverage, reshaping Top-K retrieval.
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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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·2 min
Learning AI Large Language Models from Scratch: A Complete Roadmap That Avoids the Three Biggest Mistakes
90% of AI beginners struggle with large language models due to misdirection, poor Prompt logic, and lack of real-world deployment skills. This guide covers the complete learning path from zero to practice.
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·2 min
AI Agent Primer: The Core Differences Between Workflows and Intelligent Agents — and How to Build Them
Explore the key differences between AI Agents and workflows, and how LLMs evolve from reasoning to execution. Covers ReAct, task decomposition, enterprise value, and Python+LangChain development.
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·3 min
Enterprise AI Agents in Practice: A Developer's Guide to Hermes Agent
A deep dive into Hermes Agent vs OpenCloud with real enterprise case studies across telecom, finance, and e-commerce — revealing why mastery, not tool choice, drives AI agent success.
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·2 min
15-Year-Old Trains 200M MoE Language Model from Scratch: Full Architecture & Hands-On Breakdown
A 15-year-old trained Tiny-MoE, a 200M-parameter MoE language model from scratch using free Kaggle GPUs, featuring MLA attention, RoPE+YaRN, and native PyTorch.
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·3 min
AI Agent Beginner's Guide: LLMs vs. Agents, and a Full Breakdown of Development Tools
What is an AI Agent? This guide explains the key differences between LLMs and Agents, breaks down the Agent formula (LLM + Workflow + Knowledge Base), and compares tools like Dify, Coze, LangChain, and LlamaIndex.
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·4 min
Baidu AI Automation System: Trigger Skill Chains with a Single Sentence, Say Goodbye to Manual Workflows
The Baidu AI Automation System uses departmentalized skill libraries and Agent-style orchestration to let managers trigger multi-skill workflows with one sentence—no tech background needed.
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·6 min
AI Agent Development Learning Path: Four Stages from Zero to Hands-On Projects
A systematic breakdown of the AI agent development learning path, covering four stages: fundamentals, RAG knowledge bases, tool use, multi-agent collaboration, and hands-on projects.
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·4 min
The Complete AI Agent Development Learning Path: From Zero to Enterprise-Grade Practice
A systematic AI Agent development learning path covering fundamentals, prompt engineering, tool calling, multi-agent collaboration, and hands-on practice with LangChain, CrewAI, and Dify.
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·7 min
Breaking Down the Core Skills of an AI Application Engineer: From Beginner to Industrial-Grade Deployment
Knowing how to call an API doesn't make you an AI engineer. This article breaks down the complete skill structure of an AI application engineer, covering Python fundamentals, LLM fine-tuning, Agent development, and enterprise projects.
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·7 min
Unity Custom MCP Tool Development Guide: Enabling AI Agents to Precisely Control the Editor
Learn how to develop custom tools based on the Unity MCP Relay Server, enabling AI agents like Cursor to directly control the Unity editor. Covers setup, tool building, parameter validation, and MCP Pro comparison.
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·6 min
Hands-On With Hermes Agent's Eight Major Updates: A Complete Guide to AI Automation and Self-Evolution
The Hermes Agent gets a major upgrade with eight new features: native iMessage, parallel background sub-agents, Unreal Engine MCP support, a self-evolving Skill Hub, and more. A hands-on breakdown of the core changes and their real impact on personal AI automation workflows.
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·3 min
Building a Claude Code Skill from Scratch: Let AI Auto-Generate Daily News Reports
A detailed guide on building a custom Claude Code Skill to auto-fetch, filter, and generate daily AI news reports—covering execution logic, task decomposition, HTML visualization, and source tracing.
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·4 min
Coze in Practice: A Complete Guide to Building Multi-Agent Collaboration Teams
A systematic guide to Coze's positioning and capabilities, covering Agent-building platform categories, Skill modules, workflow orchestration, and multi-Agent team building.
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·5 min
Krea 2 Turbo Style Reference LoRA: Define Your Generation Style with a Single Image
ostris releases the Krea 2 Turbo Style Reference LoRA, supporting single or multi-image style extraction for precise AI style transfer. Open-source, free, and locally deployable.
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·6 min
Getting Started with AI Agents: Principles, Frameworks, and a Guide to Building Enterprise-Grade Agents
In-depth analysis of AI Agent core principles: why LLMs need Agent technology, the evolution from Prompt to RAG to Agent, Agent Tuning methods, and enterprise cost evaluation to help you build enterprise-grade agent applications.
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