This course bridges the gap between understanding AI technology and gaining insights into user needs, model evaluation, and product design. It is ideal for product managers and cross-functional teams responsible for implementing AI solutions.
An AI feature that performs well in a demo doesn’t necessarily translate into a product that users will continue to use. *Toward the AGI Era: A Methodology for AI Product Managers* is ideal for readers who want to bridge this gap: it goes beyond discussing what models can do and incorporates user needs, user experience, evaluation, and team collaboration into the scope of product work.
The author, Ye Linzeng, also shares AI-related content under the pseudonym “Sanqiao Jun” as a technology blogger, with a focus on AI product implementation. This book was published by Tsinghua University Press on September 1, 2025, with ISBN 9787302697282. The phrase “Toward AGI” in the book’s title indicates the direction of the discussion; it does not imply that current products already possess general artificial intelligence capabilities.
The content covers changes in the product manager’s role, key AI technologies, requirements analysis, model evaluation, product design, user experience, teamwork, and business models. It places concepts such as large language models, prompts, and agents within the context of product decision-making, making it an ideal starting point for those transitioning from traditional product roles to AI product roles.
This book is suitable for product managers planning AI features, technical managers collaborating with algorithm teams, and leaders who need to translate business problems into product solutions. For pure algorithm researchers, it is not a substitute for a mathematics textbook; for readers with no product experience whatsoever, we recommend reading it in conjunction with a specific project to avoid merely memorizing framework names.
We recommend selecting a real-world task with clearly defined boundaries—such as a Q&A, document summarization, or customer service assistance—and first outlining the target users, the existing workflow, and the primary pain points. Next, define the criteria for success: Is the output correct? How much time does the user need to verify it? How does the process exit or escalate to a human agent in case of failure? And is the cost acceptable?
Validate the approach using small-scale samples, and do not mistake a single impressive response for consistent capability. For generative features, it is especially important to include error handling, human verification, and result traceability in design discussions.
Its value lies in helping different stakeholders communicate about the same product issue, rather than treating the model’s capabilities as a standalone selling point. Specific models, platform APIs, and commercial terms change rapidly, so you should still consult the latest official documentation when implementing the solution. When reading this material, focus first on identifying the methods for determining and evaluating requirements, and then update the operational details related to specific tools as needed.
If you start by reading *A Brief History of Artificial Intelligence* to build a foundation, then use this book to develop product ideas, and return to *Methods in Machine Learning* when you encounter technical issues that require a deeper understanding, you’ll create a highly complementary learning path.
Tsinghua University Press: Book Information, Author Biography, Table of Contents, and Book Cover
简体中文 简体中文 
English