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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
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.

Memory SystemObservabilityPrompt Injection Defense
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.

AgentContext EngineeringExecution Model
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 ManagementTool Orchestration
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
AI Large Model Application Practice
AI Large Model Application Practice
Jun 15, 2026 · Artificial Intelligence

Deep Dive into AgentMemory: Adding a Shared, Persistent Memory Layer for Enterprise AI Coding

AgentMemory introduces a shared, persistent memory service for AI coding agents, capturing session observations, extracting memories, lessons, and knowledge graphs, and exposing them via hooks, MCP tools, and REST APIs to prevent repeated mistakes, improve decision reuse, and enhance engineering efficiency.

AI codingAgentMemoryLLM
0 likes · 13 min read
Deep Dive into AgentMemory: Adding a Shared, Persistent Memory Layer for Enterprise AI Coding
AI Large Model Application Practice
AI Large Model Application Practice
Jun 11, 2026 · Artificial Intelligence

Ontology Meets AI Agents: From Reasoning to Enterprise Semantic Infrastructure

The article demonstrates how an ontology can serve as a business‑semantic layer for enterprise AI agents, covering multi‑relationship propagation, schema‑to‑concept mapping, cross‑system customer views, and a unified semantic query engine, while also discussing practical limits and rollout advice.

AI AgentsKnowledge GraphOntology
0 likes · 11 min read
Ontology Meets AI Agents: From Reasoning to Enterprise Semantic Infrastructure
AI Large Model Application Practice
AI Large Model Application Practice
Jun 4, 2026 · Artificial Intelligence

How Ontology Empowers Enterprise Agents Beyond Reasoning: Building a Semantic Infrastructure

The article explores three advanced ontology applications for enterprise AI agents—multi‑relationship propagation, schema‑mapping to decouple column names, and a unified semantic query engine—showing how a business‑semantic layer can replace hard‑coded logic while highlighting implementation challenges and practical start‑up advice.

Knowledge GraphOntologySemantic Layer
0 likes · 12 min read
How Ontology Empowers Enterprise Agents Beyond Reasoning: Building a Semantic Infrastructure
AI Large Model Application Practice
AI Large Model Application Practice
May 20, 2026 · Artificial Intelligence

Why AI Coding Falls Short of Its Promised Efficiency in Complex Enterprise Systems

Although AI coding agents like Claude Code and Codex promise dramatic productivity gains, the article explains that in large‑scale enterprise software the benefits are limited by unclear requirements, extensive context engineering, hidden token and rework costs, subtle bugs that pass superficial tests, and the need for strict risk‑tiered usage and human‑AI collaboration.

AI codingContext EngineeringEnterprise Software
0 likes · 18 min read
Why AI Coding Falls Short of Its Promised Efficiency in Complex Enterprise Systems
AI Large Model Application Practice
AI Large Model Application Practice
Apr 27, 2026 · Artificial Intelligence

How Graphify Becomes the “Second Brain” for AI Coding in Enterprise Legacy Systems

Graphify transforms scattered code, documentation, and business knowledge into a structured knowledge graph that serves as a “second brain” for AI coding assistants, enabling them to navigate and understand complex enterprise legacy systems, reduce token costs, and improve answer quality, as demonstrated by detailed tests on the BettaFish project.

AI codingGraphifyKnowledge Graph
0 likes · 16 min read
How Graphify Becomes the “Second Brain” for AI Coding in Enterprise Legacy Systems