While the industry has spent the last two years chasing ever larger language models, enterprises actually lack a unified, computable and evolvable semantic structure, and ontologies—re‑imagined as a semantic operating system—provide the necessary backbone for reliable, business‑aware AI deployment.
Current AI agents excel at isolated tool calls but cannot reliably deliver complex projects; this article analyzes their limitations, introduces a Component-Based Engineering (CBE) paradigm with a fixed meta‑model for formal problem modeling and automated solving, enabling systematic, verifiable, outcome‑based AI agent deployments.
A technical livestream with experts from DeepSecurity, Ping An Life and China Mobile Jiutian reveals that the real bottleneck for AI agents in enterprises is not model capability but engineering, organizational processes and risk control, and outlines concrete strategies—code graphing, layered constraints, verification loops and metric‑driven adoption—to turn probabilistic AI output into reliable, organization‑wide productivity.
The article details a systematic practice—starting from the “iceberg” challenges of finance, through data and knowledge engineering, reverse knowledge extraction with REER, multi‑dimensional synthetic data generation, prompt engineering (APO), cost‑aware fine‑tuning, inference acceleration, and emotion‑value evaluation—culminating in actionable guidelines for deploying large models in banking scenarios.
The talk outlines the AI era’s three major challenges for large language models and data (stability, 3V, resource limits), presents YashanDB’s original theory breakthroughs—including resource‑constrained computing, shared‑cluster performance and adaptive transaction scheduling—details a multi‑modal fusion architecture, introduces the KSA trustworthy knowledge‑engineering framework, and showcases real‑world deployments in smart‑city and energy domains.
The article outlines the evolution of big data in banking, compares management models for heterogeneous data, describes the shift from data engineering to knowledge engineering, introduces LLMOps for high‑quality knowledge bases, and details how integrating AI and data can enable a “one‑sentence bank” that answers queries and executes tasks.
This expert roundtable dissects why RAG often fails in production—low recall, hallucinations, cost overruns—and walks through concrete diagnostics, hybrid search designs, knowledge‑engineering tricks, GraphRAG and Agentic RAG advances, plus practical deployment, security, and cost‑optimization guidelines.
The author explains how, after ten years of writing, they analyzed their own articles, extracted evolving stylistic patterns, and engineered a modular, reusable writing skill—/phodal-writer/—that can be repeatedly loaded by AI to produce consistently structured, paced, and judgment‑rich content.
This article explains how Andrej Karpathy's LLM‑plus‑Obsidian workflow transforms large language models into continuous knowledge engineers, detailing a three‑layer architecture, core operations, practical setup steps, and open‑source tools that enable a self‑maintaining, compounding personal wiki.
The article analyzes Andrej Karpathy’s LLM‑Wiki concept, showing how turning raw materials into a continuously compiled, cross‑linked knowledge system—rather than a static note store—can empower personal and professional workflows across research, coding, health, and more.
The article examines how AI’s exponential acceleration is reshaping software development, debunking the hype that 99% of engineers will be obsolete, highlighting real data on junior job loss, the rise of knowledge engineering, technical debt risks, and the need for engineers to evolve into AI‑enabled system designers.
This article breaks down Andrew Ng and Anthropic’s Agent Skills course, explaining how organized skill folders give general AI agents domain expertise, repeatable workflows, and new capabilities, while using portable, composable design and progressive disclosure to make agents reliable, scalable, and industrial‑grade.
This article explains how AI Skills serve as structured knowledge bases that complement, rather than replace, Model Context Protocols, enhance Retrieval‑Augmented Generation, and drive three major trends—standardized agent stacks, low‑code knowledge engineering, and the emergence of personal AI agents.
This article surveys ontology quality evaluation by outlining key metrics such as consistency, completeness, and coverage, and reviewing five major assessment approaches—including corpus‑based, gold‑standard, metric‑driven, rule‑based, and application‑driven methods—while highlighting representative tools, open‑source implementations, and future research challenges.
The article outlines three post‑RAG knowledge‑engineering approaches—In‑Context Learning with dynamic few‑shot selection, Online Learning encompassing Meta‑Learning and Lifelong Learning to quickly adapt to new tasks, and the Small Language Model path that combines fine‑tuned task‑specific experts with LLM‑SLM collaboration for efficient, privacy‑preserving inference.
Experts from Dipu, Ant Financial, iKang, and Zhihu discuss practical strategies for improving large model performance, covering RAG, tool‑using, offline knowledge engineering, multimodal training, evaluation metrics, and future trends, while sharing case studies from manufacturing, healthcare, retail, and C‑end applications.
The article outlines the DKCF (Data‑Knowledge‑Collaboration‑Feedback) trustworthy framework presented at the 2024 Shanghai Cybersecurity Expo, detailing challenges of large AI models, four key trust factors, and Ant Group's practical security implementations for professional AI deployments.
The article reviews the history and current state of NLP, compares symbolic deep‑parsing and neural pre‑trained approaches, discusses the knowledge‑bottleneck and low‑code trend, and illustrates semi‑automated, low‑code NLP deployment in the financial domain while pondering future integration of symbolic and neural methods.
This comprehensive report analyzes the evolution of knowledge graphs, reviews achievements of the first half, and examines the challenges and opportunities of the emerging second half, highlighting shifts from large‑scale simple applications to complex, expert‑driven scenarios, and outlining strategies for representation, acquisition, and application in the era of big data and AI.
This article explains how Alibaba's Image & Beauty team designs and iterates a practical fashion image dataset by aligning data purpose, integrating professional knowledge, handling sample scarcity and structured noise, and defining fine‑grained evaluation metrics to enable AI models that truly understand clothing.
The December 20 Knowledge Graph symposium in Hangzhou, organized by Alibaba and the Chinese Society of Computational Linguistics, gathered leading Chinese scholars who discussed the pivotal role of massive knowledge graphs in AI, natural language processing, knowledge engineering, reasoning, and data‑driven intelligence.
