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AI Large Model Application Practice
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AI Large Model Application Practice

Focused on deep research and development of large-model applications. Authors of "RAG Application Development and Optimization Based on Large Models" and "MCP Principles Unveiled and Development Guide". Primarily B2B, with B2C as a supplement.

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Recent Articles

Latest from AI Large Model Application Practice

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AI Large Model Application Practice
AI Large Model Application Practice
Aug 31, 2026 · Artificial Intelligence

What Is an Ontology? 9 Questions to Understand Ontologies & Their Role in AI Agents

This beginner-friendly guide explains ontologies through nine key questions, covering their definition, difference from databases and knowledge graphs, standards like RDF and OWL, a telecom domain example, integration with AI agents, and practical steps to build a minimal viable ontology for enterprise use.

AI agentOWLOntology
0 likes · 33 min read
What Is an Ontology? 9 Questions to Understand Ontologies & Their Role in AI Agents
AI Large Model Application Practice
AI Large Model Application Practice
Aug 17, 2026 · Artificial Intelligence

From Sketching a Graph to Full‑Scale Graph Engineering: Key Practices

The article examines Graph Engineering as the disciplined process of turning multi‑agent collaboration diagrams into reliable, observable, and recoverable production systems, covering basic coordination patterns, state sharing, failure handling, observability, and a comparative look at leading frameworks such as LangGraph, Google ADK, OpenAI Agents SDK, and Claude Dynamic Workflows.

Failure RecoveryGraph EngineeringMulti-Agent Systems
0 likes · 17 min read
From Sketching a Graph to Full‑Scale Graph Engineering: Key Practices
AI Large Model Application Practice
AI Large Model Application Practice
Aug 10, 2026 · Artificial Intelligence

Is Graph Engineering Just Repackaged Old Tech or the Next Step for Powerful AI Agents?

The article explains that Graph Engineering does not introduce new technology but redefines how increasingly capable AI agents are organized, contrasting it with earlier Loop Engineering, outlining its core components, practical examples, and the specific scenarios where a graph‑based approach becomes essential.

AI WorkflowGraph EngineeringHarness Engineering
0 likes · 14 min read
Is Graph Engineering Just Repackaged Old Tech or the Next Step for Powerful AI Agents?
AI Large Model Application Practice
AI Large Model Application Practice
Aug 3, 2026 · Artificial Intelligence

Deep Dive into LLM Wiki Engineering: AI Coding, Obsidian Integration, and RAG Collaboration

This article explains how to build and maintain an LLM‑powered knowledge base (LLM Wiki) for AI coding agents, shows practical workflows using Obsidian and custom agents, and compares the governance‑focused Wiki approach with retrieval‑augmented generation, highlighting trade‑offs, metadata design, and integration patterns.

AI codingAgentKnowledge Management
0 likes · 16 min read
Deep Dive into LLM Wiki Engineering: AI Coding, Obsidian Integration, and RAG Collaboration
AI Large Model Application Practice
AI Large Model Application Practice
Jul 27, 2026 · Artificial Intelligence

Deep Dive: Building Reliable Enterprise Agent Knowledge Bases with LLM Wiki & Google OKF

The article analyzes why traditional RAG pipelines struggle with reliable, exploratory queries, introduces LLM Wiki as a method for structuring raw materials into a navigable knowledge map, explains Google’s Open Knowledge Format (OKF) as an interoperable markdown specification, and outlines a six‑step agent workflow for creating and maintaining enterprise knowledge bundles.

AgentGoogle OKFKnowledge Management
0 likes · 12 min read
Deep Dive: Building Reliable Enterprise Agent Knowledge Bases with LLM Wiki & Google OKF
AI Large Model Application Practice
AI Large Model Application Practice
Jul 13, 2026 · Artificial Intelligence

20 Essential Agent Engineering Concepts for 2026: Making Agents Practical, Scalable, and Deployable

The article breaks down ten core engineering pillars for production‑grade AI agents—including tool calling with MCP, reusable skills, persistent memory, multi‑agent collaboration, workflow orchestration, hooks, observability, sandboxing, prompt‑injection defense, and the role of forward‑deployed engineers—to help turn demo agents into reliable, enterprise‑ready systems.

Agent EngineeringPrompt Injection DefenseWorkflow Orchestration
0 likes · 21 min read
20 Essential Agent Engineering Concepts for 2026: Making Agents Practical, Scalable, and Deployable
AI Large Model Application Practice
AI Large Model Application Practice
Jul 6, 2026 · Artificial Intelligence

20 Must‑Know Agent Engineering Concepts for 2026 (Runtime Mechanisms)

This article breaks down the 20 core concepts essential for building enterprise agents in 2026, covering the agent definition, harness framework, execution models, loop engineering, state and context management, prompt caching, ontology, and live retrieval, each illustrated with practical examples and engineering tips.

AgentHarnessLLM
0 likes · 17 min read
20 Must‑Know Agent Engineering Concepts for 2026 (Runtime Mechanisms)
AI Large Model Application Practice
AI Large Model Application Practice
Jun 30, 2026 · Artificial Intelligence

Why Your AI Coding Costs Are Soaring and 10 Engineering Tricks to Cut Token Usage

The article explains why AI coding bills are rising rapidly as models handle larger contexts and more complex tasks, then presents ten concrete engineering methods—such as context cleanup, code navigation, planning, tool segregation, input noise reduction, prompt caching, model layering, on‑demand context loading, output trimming, and open‑source token compressors—to systematically reduce unnecessary token consumption.

AI codingContext ManagementModel layering
0 likes · 20 min read
Why Your AI Coding Costs Are Soaring and 10 Engineering Tricks to Cut Token Usage
AI Large Model Application Practice
AI Large Model Application Practice
Jun 22, 2026 · Artificial Intelligence

8 Crucial Questions to Understand Loop Engineering and the New Agent Paradigm

The article breaks down Loop Engineering—a new paradigm for AI agents—by exploring why it emerged, defining its scope, distinguishing it from Agent Loops and Context/Harness Engineering, detailing its building blocks, tools, applicability criteria, and the risks and limitations of fully autonomous loops.

AI agentsAgent LoopAutomation
0 likes · 16 min read
8 Crucial Questions to Understand Loop Engineering and the New Agent Paradigm