Focuses on AI-powered enterprise digitalization, sharing product and technology practices. Covers AI use cases, technical architecture, product design examples, and industry trends. Aimed at developers, product managers, and digital transformation professionals, providing practical solutions and insights. Uses technology to drive digitization and AI to enable business innovation.
Over six months the team built an AI agent that now answers more than 70% of technical support queries by grounding responses in system data, a curated knowledge base, and a tiered permission model, while also exposing growing technical debt and maintenance challenges.
Prompt engineering serves as the bridge between humans and AI, requiring cognitive decomposition, instruction engineering, and model alignment; the article outlines essential skills, precise language, scenario adaptation, iterative optimization, ethical safeguards, symbol conventions, template examples, tuning tools, FAQs, and learning resources.
The article analyzes how AI has evolved from a code‑completion aid to a foundational operating system that forces product‑research teams to redesign the entire requirement‑to‑delivery value stream, outlining practical boundaries, pilot implementation, organizational role changes, metric shifts, and risk governance.
The article outlines a four‑layer framework—process, technology, risk, and culture—to help enterprises implement AI‑in‑the‑loop safely, ensuring AI assists decisions while humans retain final authority, with concrete governance, data, and organizational practices.
The article outlines a comprehensive AI engineering methodology—including the TPMR framework, an AI‑driven development lifecycle, talent transformation from co‑pilot to AI pilot, and a practical enterprise adoption roadmap—to move generative AI and large models from experimental prototypes to production‑grade systems.
