One of China's top product manager communities, gathering 210,000 product managers, operations specialists, designers and other internet professionals; over 800 leading product experts nationwide are signed authors; hosts more than 70 product and growth events each year; all the product manager knowledge you want is right here.
Over the past year five AI product managers left their positions, revealing that merely knowing large‑model tech is insufficient; true success requires owning unique data, deep business understanding, controllable delivery, and demonstrable cost or revenue impact.
Although AI can now write PRDs, conduct competitor analysis, and generate prototypes, the article argues that product managers remain hard to replace because their core value lies in making judgments amid ambiguity, defining worthwhile problems, designing feedback loops, reshaping interactions, and owning outcomes as execution becomes cheaper.
The article explains why many AI Skills remain draft‑level, outlines three concrete scenarios—product, design, and operations—to transform a usable Skill into a reliable one, and provides a step‑by‑step checklist for description, workflow, and management to achieve stable, repeatable AI assistance.
The article explains how product managers often waste resources chasing 100% functional coverage, and shows that mature PMs use a 60%‑for‑80% approach—freezing low‑frequency, high‑cost scenarios, applying ROI formulas, scoring cards, and concrete ERP examples—to deliver core value efficiently.
The article explains why repeating prompts is inefficient, defines a Skill as a reusable SOP for AI, and presents three concrete methods—reverse‑engineering from results, completing a task then summarising, and writing a work‑instruction for AI optimisation—along with templates, storage tips, and real‑world Skill examples.
The article debunks the buzzword “Product Sense” and argues that a competent AI product manager must master three core disciplines—computer fundamentals, psychology, and management—to make logical, user‑focused decisions and earn technical teams’ respect.
After two months of AI Agent projects, I distilled five counter‑intuitive iron rules—decision‑makers avoid exhibition booths, trust demands deep conversations, my strength lies with hour‑long clients, technical details can be outsourced while business insight stays personal, and acquisition must move from breadth to depth.
The article explains how AI product managers can avoid costly product failures by designing a comprehensive, multi‑dimensional evaluation framework that guides development, quantifies progress, and builds trust with users and stakeholders.
The article explains that prompt engineering is essentially a demand‑expression technique that helps large language models understand user intent more accurately, reducing guesswork, defining task boundaries, and enabling better evaluation, while also outlining practical design methods for AI products to guide users toward clearer prompts.
After a project finishes, teams often keep visible artifacts like screenshots and acceptance documents, but the truly valuable knowledge lies in the judgments, constraints, and reusable conditions behind those artifacts; this article explains how product managers can identify, evaluate, and correctly allocate that value to make future projects lighter, not heavier.
