AI Product Management: Strategy, Datasets, and LLM Integration

As artificial intelligence reshapes industries, businesses need product managers who can bridge the gap between technical AI capabilities and real-world user needs. This course guides you through the foundational concepts of managing AI-driven products from initial concept to launch. You will transition from traditional product management to leading cutting-edge AI initiatives. You will understand how to evaluate machine learning models, design data strategies, and leverage generative AI frameworks to build scalable, ethical, and highly effective products. What you'll learn: - Understand the core terminology of machine learning, deep learning, and generative AI - Design dataset specifications and data collection strategies to train reliable AI models - Create comprehensive Product Requirement Documents (PRDs) tailored for AI and LLM integration - Evaluate AI model performance using key metrics like precision, recall, and accuracy without needing a coding background - Formulate product strategies that incorporate modern LLM patterns, including prompt engineering and Retrieval-Augmented Generation (RAG) - Address ethical considerations, bias, and data privacy in AI product development The course begins with essential AI terminology and foundational concepts before moving into practical dataset design, roadmapping, and generative AI product strategies. Through clear written explanations and structured text-based scenarios, you will learn to collaborate effectively with data science and engineering teams. This course is designed for aspiring AI product managers, traditional PMs looking to transition, and business leaders wanting to understand the AI product lifecycle. No programming experience or technical background is required. Start reading today to build the skills needed to lead the next generation of AI products.