S2 | The AI SPRINT ⚡️ | Building AI Products | HELLOPM скачать в хорошем качестве

S2 | The AI SPRINT ⚡️ | Building AI Products | HELLOPM

Welcome to The AI Sprint by HelloPM – a free, 4-day learning series designed to help you master AI Product Management, Generative AI, and Large Language Models (LLMs). In this second session, we go deeper into building AI products, understanding feasibility and viability, case studies like Granola and ChatPRD, and the cost of inference. This session moves from theory into practical frameworks that every product manager must know. What you’ll learn in this session: • Predictive AI vs Generative AI and why prediction still powers GenAI • The GenAI Value Stack: Infrastructure, Models, Applications, Services • Core vs Applied AI Product Managers and where most opportunities exist • How Large Language Models (LLMs) actually work step by step • Four risk framework (Valuable, Usable, Viable, Feasible) for AI product design • Case Study 1: Granola AI Notetaker – tech stack, usability, token cost • Case Study 2: ChatPR – building faster PRDs with LLMs • Cost of inference and token economics for AI PMs • Business models: funding, penetration pricing, and ROI for AI products Chapters 00:00 Welcome Back to AI Sprint: Cities, Roles, and Recap 05:40 Three Mindset Rules: Fundamentals, Curiosity, and Reflection in AI PM 14:28 Predictive vs Generative AI Explained with Use Cases 22:35 GenAI Value Stack: Infrastructure, Models, Applications, and Services 31:50 Types of AI Product Managers: Enabled, Core, and Applied 39:42 How Large Language Models Work: From Tokens to Transformers 49:15 Base Models vs Post-Training: Turning LLMs into Helpful Assistants 58:20 Building AI Products: Marty Cagan’s Four Risks Framework 01:08:44 Case Study 1 – Granola AI Notetaker: Tech, Usability, Value 01:21:12 Token Costs, Inference, and Economics of AI Products 01:31:25 Why OpenAI and AI Giants Focus on Land Grab Strategy 01:40:56 Case Study 2 – ChatPR: Automating PRDs with LLMs 01:50:20 Lessons for AI PMs: Value, Usability, Feasibility, and Viability 01:58:10 Talent and Learning: Pursued Interest and Course Correction 02:04:32 Final Notes: Inspiration is Perishable, Next Session Preview ‣ Get all recordings & slide deck from here → https://hellopm.link/tas25recsY2 ‣ Participate in the AI Sprint Vibe-athon Case Challenge Competition: https://hellopm.link/tas25caseY2 ‣ Want to go deeper? Explore HelloPM’s flagship AI Product Management Program here → https://hellopm.co/?refer=yts2 Don’t forget to subscribe for more sessions, case studies, and hands-on strategies in AI Product Management. Keywords AI product management, generative AI, predictive AI, large language models explained, how LLMs work, transformers in AI, AI note taking apps, Granola AI case study, ChatPR product teardown, AI for product managers, AI sprint HelloPM, AI value stack, core vs applied AI PM, AI PM roadmap, AI career for product managers, GPT explained, reinforcement learning with human feedback, AI tools for PMs, cost of inference in AI, Marty Cagan product principles, building AI products, AI product strategy, token economics, AI monetization models, penetration pricing in AI, future of AI in product management Tags / Hashtags #AIProductManagement #GenerativeAI #LargeLanguageModels #ArtificialIntelligence #ProductManagement #HelloPM #AISprint #ChatGPT #AIForProductManagers #AIProductStrategy #TokenEconomics #TechCareers #FutureOfWork