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AI Large-Model Wave and Transformation Guide
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AI Large-Model Wave and Transformation Guide

Focuses on the latest large-model trends, applications, technical architectures, and related information.

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Latest from AI Large-Model Wave and Transformation Guide

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AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 9, 2026 · Industry Insights

Inside DingTalk’s Overwork Crisis: How Extreme Management Prompted a Rapid Leadership Overhaul

The article chronicles DingTalk’s extreme overtime policies—such as the 9127 schedule, “Wangshou” night‑stay rule, and daily‑package pressure—how they harmed employee health and product metrics, triggered a wave of resignations, and forced Alibaba to replace leadership and roll out sweeping reforms within a week.

AlibabaDingTalkEmployee Health
0 likes · 8 min read
Inside DingTalk’s Overwork Crisis: How Extreme Management Prompted a Rapid Leadership Overhaul
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 9, 2026 · Industry Insights

What Companies Will Actually Profit in the AI Era? Lessons from 250 Years of Tech Cycles

The article examines 250 years of technology revolutions—from electricity to the internet—to reveal a recurring four‑stage cycle of boom, speculation, crash, and lasting infrastructure, arguing that today’s AI bubble will similarly seed the foundational assets that future winners will exploit.

AIInfrastructurehistorical analysis
0 likes · 9 min read
What Companies Will Actually Profit in the AI Era? Lessons from 250 Years of Tech Cycles
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 9, 2026 · Artificial Intelligence

Why Ontology Can Be More Precise Than RAG While Requiring Less Engineering Effort?

The article compares RAG and ontology‑based knowledge graphs, showing that although both appear simple in demos, ontology often delivers higher precision with greater engineering cost, and argues that true simplification comes from architectural trade‑offs rather than choosing a supposedly "simple" technology.

AIComplexityKnowledge Graph
0 likes · 9 min read
Why Ontology Can Be More Precise Than RAG While Requiring Less Engineering Effort?
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 9, 2026 · Artificial Intelligence

Why Enterprise AI Needs All Three Legs: Data, Agent, and FDE

The article explains how a large enterprise succeeded in AI‑enabled sales by cleaning five years of data, deploying a dedicated AI agent for each of eleven sales stages, and using Front‑end Deployment Engineers to translate expert knowledge into repeatable processes, showing that missing any of these three components makes the system limp.

AI deploymentAgent ArchitectureBusiness Process Automation
0 likes · 9 min read
Why Enterprise AI Needs All Three Legs: Data, Agent, and FDE
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 9, 2026 · Artificial Intelligence

Why Explaining Ontology Beats Technology in AI Agent Deployments

The article argues that the biggest hurdle in applying ontology to AI agents is not the technical effort but convincing business stakeholders, and it offers three practical tricks to embed ontologies silently into prompts, guard against LLM hallucinations, and translate formal constraints into actionable rules.

AI agentsLLM hallucination mitigationOntology
0 likes · 8 min read
Why Explaining Ontology Beats Technology in AI Agent Deployments
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 9, 2026 · Artificial Intelligence

Semantic Layer, Ontology, and Enterprise Context Layer: How They Nest for AI‑Ready Data

The article explains why most AI projects fail due to poor data structure, then breaks down the three nested layers—Semantic Layer, Ontology, and Enterprise Context Layer—showing their distinct purposes, how they build on each other, real‑world examples, governance challenges, and why proper investment sequencing matters for AI‑ready data infrastructure.

AI data architectureData GovernanceKnowledge Graph
0 likes · 23 min read
Semantic Layer, Ontology, and Enterprise Context Layer: How They Nest for AI‑Ready Data
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 8, 2026 · Artificial Intelligence

From Controlled Vocabularies to Ontologies: Tracing the Evolution of Knowledge Representation

The article examines the historical progression from simple controlled vocabularies through taxonomies and thesauri to formal ontologies and knowledge graphs, highlighting how each stage adds semantic depth, formal constraints, and machine‑readable logic for richer knowledge modeling and inference.

Controlled VocabularyKnowledge GraphOWL
0 likes · 14 min read
From Controlled Vocabularies to Ontologies: Tracing the Evolution of Knowledge Representation
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 8, 2026 · Artificial Intelligence

AI Hallucinations in Text-to-SQL: Four Common Pitfalls and How to Mitigate Them

Large language models generate SQL by predicting tokens rather than truly understanding databases, leading to four categories of hallucinations—factual, logical, instructional, and knowledge‑boundary—each with concrete examples, and the article outlines five practical strategies such as schema‑pre‑alignment, execute‑then‑rerank, compiler feedback, real‑time schema sync, and human verification to curb these errors.

AI hallucinationRAGSQL validation
0 likes · 9 min read
AI Hallucinations in Text-to-SQL: Four Common Pitfalls and How to Mitigate Them
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 7, 2026 · Artificial Intelligence

The Illusion of Chinese Tech Giants' 'Ontology Products': What’s Really Missing?

The article critically dissects the hype around Chinese tech giants' so‑called ontology products, revealing that their knowledge‑graph tools lack formal reasoning, their "full‑stack self‑developed" stacks are merely patched ecosystems, and their AI agents rely on statistical tricks rather than true symbolic world models.

AI hypeKnowledge GraphLarge Language Model
0 likes · 10 min read
The Illusion of Chinese Tech Giants' 'Ontology Products': What’s Really Missing?