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.
The article analyzes why embedding a generic ChatBot in niche applications leads to low engagement, shares the author's experience building a matrix of one general and five specialized assistants in a news app, and provides a decision framework and practical pitfalls for product teams.
The article explains why algorithm engineers resent product managers who treat models as black‑boxes, make vague data‑blind demands, and ignore experimental cycles, and it offers three concrete practices—feature‑focused communication, metric quantification, and respecting experiment timelines—to become a trusted teammate.
This article walks through a real‑world AI product case study where a cross‑border e‑commerce photo‑generation system was built from scratch, cutting per‑image cost from ¥65 to under ¥0.5, shrinking turnaround from days to minutes, and achieving 65% business adoption through a disciplined seven‑step process.
By 2026, AI product managers must shift from merely using models to delivering stable, valuable results, mastering seven core abilities—demand judgment, evaluation-driven iteration, context design, RAG strategy, agent orchestration, solution planning, and rapid Vibe Coding—to close the loop between business needs and AI capabilities.
Product managers are increasingly anxious about how AI Agents will reshape product logic, prompting a shift from button‑driven apps to intent‑driven assistants; this article breaks down the four core modules of an Agent, illustrates a market‑analysis workflow, and outlines design trade‑offs and common pitfalls.
This article shares a step‑by‑step framework for systematically discovering hidden user requirements in rule‑intensive B2B applications, using real‑world case studies, the McGovern iceberg model, four investigative dimensions, and funnel‑style questioning to turn vague requests into concrete, auditable features.
The article analyzes how Codex App surpasses Claude Code by offering a sandboxed environment, permission controls, task‑list UI, planning and steering modes, Git rollback, worktree isolation, cloud integration, plugins, skills and MCP, turning the AI coding assistant into a controllable engineering workbench for a broader range of users.
The author recounts an interview where the question "How do you use AI in your work?" was answered with a concrete, step‑by‑step AI‑assisted workflow that turned a tool outage into a stable, reusable process, demonstrating the value of problem definition over mere tool mentions.
The article analyzes the emerging "skill" trend—encapsulating human expertise as callable modules—and explains how it reshapes product design, turns AI into an autonomous project manager, and forces humans to become structured, API‑like nodes within collaborative systems.
The article recounts six concrete pitfalls encountered while delivering an AI‑native B2B software module in 30 days, from chasing flashy AI features to neglecting compliance, and shows how focused problem definition, robust PRD design, MVP prioritisation, and strict demand validation turned failures into a market‑ready product.
