Articles

This article provides AI Product Managers with a practical decision-making framework for model selection, namely the Three Key Questions. First, it emphasizes that model selection should be scenario-driven, accurately addressing business pain points, and points out the trend of shifting from single functionalities to a multi-agent paradigm. Second, it delves into cost considerations, pointing out the huge expenses of self-developed general large models, advocating the use of low-cost APIs to concentrate resources on scenario-based implementation and product experience, citing ByteDance's cost reduction strategy as an example. Finally, it emphasizes the importance of data and boundaries, including data quality and compliance, as well as the critical role of AI explainability (XAI) in high-risk areas. The article aims to help AI Product Managers make strategic model decisions, rather than focusing solely on technical aspects.

This article is written by a senior Product interviewer, who, through the experience of interviewing 300 Product candidates, deeply analyzes the four typical manifestations of insufficient thinking skills in Product interviews, namely "actions without conclusions", "conclusions without logic", "shallow thinking despite logic" and "deep thinking but not to the point". The article emphasizes that the core of a Product interview is to examine the candidate's problem-solving logic, rather than simply listing past experiences and actions. Finally, the article suggests that Product candidates should deeply reflect on and summarize past requirements from four dimensions: "What core problem was solved?", "Why was this solution chosen?", "What variables were considered?", and "What were the final effects and differences?". This will improve the depth of thinking and efficiency of expression in the interview.

The article deeply analyzes the fierce competitive landscape of the current Chinese Internet food delivery market. The author points out that the capital market is cold, and financial strength is the key to determining the outcome of long-term competition. Using data on the decline in the market value of JD.com and Meituan and the increase in the market value of Alibaba, it vividly reveals the capital strategies of key players in the food delivery war. The article also discusses the trend of Internet subsidies entering the 100 billion yuan level, and analyzes in detail how Taobao Flash Sale uses its e-commerce genes and strong financial resources to quickly seize market share and reclaim the low-price mindshare through saturation attacks and by framing food delivery as part of the e-commerce landscape. Finally, the article looks forward to the future of the industry, believing that Double 11 is a short-term challenge for Taobao Flash Sale, and the Spring Festival in 2026 may be a potential turning point for the food delivery war, and points out that Pinduoduo is Alibaba's biggest potential threat.

Through personal experience of being subjected to psychological manipulation by a leader, this article reflects on and reveals five core truths faced by knowledge workers: over-reliance on AI tools eroding independent thinking, fragmented information impairing focus, insufficient knowledge application and synthesis, thinking easily trapped in fixed patterns, and difficulty in practicing structured thinking. Additionally, it emphasizes attitude as a key enabler of performance. The author suggests AI as an auxiliary tool, active focus management, establishing an efficient knowledge system, daring to break and reconstruct thinking patterns, and refining structured expression skills, while being vigilant about the vicious cycle caused by attitude problems. This aims to inspire and direct readers in identifying and responding to workplace challenges.

Addressing the complex issue of Bug Fix decisions near product launch, this article provides product managers with a practical decision-making framework. The article begins with a vivid dialogue case, revealing the common conflicts and communication skills between product managers and developers on Bug Fix issues, emphasizing the effectiveness of non-coercive communication methods (such as emoticons) on promoting collaboration. Subsequently, the article details how to use a Risk Matrix to visually assess the severity and likelihood of different Bugs, to determine Bug Fix priorities, and provides specific steps for creating and using a Risk Matrix. Finally, it supplements auxiliary decision-making factors such as resource assessment, user impact assessment, timeline assessment, and alternative solution assessment, aiming to help product managers ensure on-time delivery and maintain high Product Launch quality.

The article deeply analyzes the innovative mechanism of Amap's 'Street Sweeping List', aiming to address the trust crisis caused by score inflation and the rampant fake reviews in traditional UGC lists in the AI era. The core of the Amap Street Sweeping List is to build a scoring system based on 'real behavior' and 'credit'. It utilizes Amap's massive user navigation, search, store visit, and collection behavior data to quantify the real intention of users demonstrating genuine interest through their actions, and on this basis, uses AI Risk Control to identify and filter out false reviews. At the same time, the introduction of the Alipay Sesame Credit system weights the evaluations of high-credit users, effectively enhancing the list's ability to resist score inflation. This new mechanism aims to allow businesses to return to the essence of service, consumers to obtain a more reliable decision-making basis, thereby promoting a positive feedback loop of the local life service ecosystem.

This article provides an in-depth analysis of the 'metric hallucination' phenomenon in product management, drawing parallels with the 'hallucinations' seen in large language models under single-metric evaluation. It argues that when OKRs overemphasize single quantitative metrics, product teams risk prioritizing 'metric optimization' over genuine 'value creation.' Through case studies of an education App, a fresh food e-commerce platform (GMV), and an information product (click volume), the article illustrates how short-term data target attainment can mask or exacerbate underlying business issues. To counter this 'hallucination,' the article proposes three key strategies: establishing a three-dimensional metric system ('value, efficiency, and risk') to balance long-term value with short-term efficiency; implementing 'metric granularity management' to differentiate between short-term optimizable metrics and value-driven metrics; and building an 'anti-fragility assessment mechanism' to encourage transparency, strategic flexibility, and reverse attribution analysis. The article concludes that true product competitiveness lies in the ability to see beyond data, focus on user value, and avoid the pitfalls of 'data hallucination'.

This article provides an in-depth analysis of user dissatisfaction on Xiaohongshu caused by celebrity endorsements and excessive advertising. It argues that the issue stems from the platform's aggressive monetization strategy and a flawed recommendation algorithm, rather than simply celebrity hype. Xiaohongshu's merging of e-commerce and advertising business lines to boost monetization has led to a higher proportion of commercial content in user feeds and significant homogenization, negatively impacting user experience and purchase decisions. The article emphasizes that Xiaohongshu, as a community, should prioritize 'authentic sharing,' ensuring commercial content aligns with this principle to maintain user trust. The author remains optimistic about Xiaohongshu's future, viewing this as a dynamic adjustment process. The platform's vitality in attracting merchants and users will enrich the ecosystem, provided that growth and expansion are rooted in its community essence.

The article delves into how DingTalk AI Table empowers product managers, liberating them from the predicament of being held accountable by traditional spreadsheet tools and transforming them into efficiency experts. Through two core scenarios—user feedback collection and stable system operation—the article elaborates on the pain points of traditional workflows and demonstrates how DingTalk AI Table (including AI Field Agent and DingTalk Robot) enables intelligent information classification, automated processing, real-time alerts, and data visualization. These features significantly improve work efficiency and decision-making quality, allowing product managers to focus more on creative work. The article emphasizes that DingTalk AI Table not only enhances individual efficiency but also reconstructs the data flow logic of organizational collaboration, likening digital office to a well-designed infrastructure for data flow, ultimately allowing data to serve people and achieve the common growth of individuals and organizations.

This article uses the dilemma encountered by a friend, Xiaoming, in the workplace as an example to elaborate on the awkward situation where a leader takes on tasks outside the scope of subordinates' responsibilities and ultimately asks the subordinate to reject the task. Addressing this common workplace problem, the author proposes four actionable strategies: First, readers are advised to take a step back and view the task rejection as a normal assignment from an objective standpoint; second, it is emphasized to look beyond the surface and deeply analyze the leader's true motives and potential benefits behind their behavior; third, subordinates are encouraged to practice upward management by openly expressing their thoughts and concerns; finally, if the first three points are ineffective, it is recommended to accept and execute the task with a normal mindset. The article aims to help professionals, especially Product Managers, navigate complex interpersonal dynamics and work arrangements effectively.