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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
Feb 19, 2026 · Artificial Intelligence

When Should You Add a Knowledge Graph? 6 Practical Decision Criteria

This article outlines six concrete criteria—relationship‑centric data, reproducible reasoning, evolving schemas, multi‑hop queries, explainable decisions, and cross‑system data integration—to help engineers decide whether a knowledge graph is the right solution or if a relational database will suffice.

AI engineeringData IntegrationExplainability
0 likes · 15 min read
When Should You Add a Knowledge Graph? 6 Practical Decision Criteria
AI Large Model Application Practice
AI Large Model Application Practice
Feb 10, 2026 · Artificial Intelligence

How OpenClaw Secures Production‑Grade AI Agents with Zero‑Trust Tool Policies

This article dissects OpenClaw’s engineering techniques for building robust, production‑level AI agents, covering zero‑trust tool policies for security, markdown‑based memory management, cost‑aware reasoning levels, and controlled sub‑agent collaboration to ensure safety, efficiency, and reliability.

AI AgentsSecuritycost optimization
0 likes · 12 min read
How OpenClaw Secures Production‑Grade AI Agents with Zero‑Trust Tool Policies
AI Large Model Application Practice
AI Large Model Application Practice
Feb 4, 2026 · Fundamentals

Spec‑Driven Development: Harnessing AI Code Generation with Controlled Engineering

This article explains why AI‑assisted coding needs a disciplined, spec‑driven workflow, introduces the SDD methodology, outlines its core principles, tool ecosystem, step‑by‑step process, a hands‑on SpecKit + Copilot example, and the organizational changes required for successful adoption.

AI codingAutomationGitHub Copilot
0 likes · 20 min read
Spec‑Driven Development: Harnessing AI Code Generation with Controlled Engineering
AI Large Model Application Practice
AI Large Model Application Practice
Jan 26, 2026 · Artificial Intelligence

Why Enterprise AI Agents Fail and How Ontology Can Fix Them

This article examines why most enterprise AI agents stumble—due to hallucinations, semantic mismatches, and lack of explainability—then introduces ontology as a semantic layer that structures business concepts, rules, and constraints to enable reliable reasoning, centralized rule management, and transparent AI behavior.

AgentOntologyenterprise-ai
0 likes · 17 min read
Why Enterprise AI Agents Fail and How Ontology Can Fix Them
AI Large Model Application Practice
AI Large Model Application Practice
Jan 13, 2026 · Artificial Intelligence

Why MemOS Is the Next‑Generation Memory OS for AI Agents

This article explains MemOS’s novel approach to treating AI memory as an operating‑system resource, detailing its layered architecture, core modules, three memory forms, and practical SDK usage for cloud or self‑hosted deployments, while highlighting performance benefits and engineering constraints.

AI memoryAgent architectureKnowledge Base
0 likes · 17 min read
Why MemOS Is the Next‑Generation Memory OS for AI Agents
AI Large Model Application Practice
AI Large Model Application Practice
Jan 1, 2026 · Artificial Intelligence

Why Single-Head Attention Falls Short and Multi-Head Saves the Day

This article explains the inherent limitations of single-head attention in Transformers, illustrates them with a linguistic example, and then details how multi-head attention works through independent projection matrices, splitting and concatenation, ultimately boosting model expressiveness, robustness, and interpretability.

AIAttentionmulti-head
0 likes · 9 min read
Why Single-Head Attention Falls Short and Multi-Head Saves the Day