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 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.
This article systematically presents ten large‑model training and fine‑tuning methods—from full‑parameter finetuning to parameter‑efficient PEFT—detailing their principles, suitable scenarios, step‑by‑step workflows, code examples, and practical selection guidance for AI product managers.
The article walks through model training, validation, ensemble learning, and deployment from an AI product manager’s viewpoint, using a churn‑prediction case to illustrate decision boundaries, metric choices, industry‑specific algorithm trade‑offs, cost considerations, and practical serving options.
The article presents a decision framework for AI product managers, mapping workflow determinism and context certainty to four technical patterns—traditional RPA + AI, single Agent + RAG/knowledge graph, end‑to‑end RL Agent, and multi‑Agent collaboration—each with concrete use‑case examples and selection guidelines.
In a world where AI hiring pipelines scan resumes at 487 per second and discard 83% within 11.4 seconds, this guide shows product managers how to build a relevance‑first, data‑driven resume using deep project stories, the STAR framework, quantifiable results, and targeted skill showcases to dramatically boost interview conversion.
The article argues that AI has democratized product creation, turning execution into a low‑cost task, but makes taste—the ability to judge what to build, what adds value, what is truly usable, and what users will pay for—the decisive competitive edge, and outlines four concrete discriminative skills AI product managers need to develop.
The author recounts a detailed AI product manager interview, sharing personal background, the interviewer's probing questions about AI understanding and product strategy, feedback on superficial answers, core competency expectations, the company's knowledge‑base product, challenges faced, and key takeaways for aspiring AI product managers.
The article outlines a step‑by‑step framework for dissecting a product’s core value, business model, user experience, optimization, innovation, market environment, and technical feasibility, using a browser example to illustrate each analytical dimension.
