Agentic AI PM Live Lab: Transformers Explained for PMs

Sign up to get my learning resources: https://forms.gle/sRNjXnsurNxNAUQW7 This video breaks down the transformer architecture at a practical, high level: tokenization → embeddings + positional encoding → self‑attention (what to “pay attention” to) → a neural network that outputs probabilities over the vocabulary to predict the next word—repeated again and again to generate text. It also clarifies the encoder/decoder setup and why GPT-style models typically use the decoder-only portion for generation. The session ends with recommended resources (videos + a blog) to dive deeper into query/key/value math, attention, and how transformers scale into LLMs. 00:07 - Intro: What a transformer is 00:29 - Tokenization → tokens 00:33 - Embeddings + positional encoding 00:58 - Multi-head attention output → neural network 01:09 - Probabilities over vocabulary → next-word prediction loop 02:07 - Self-attention (importance/relationships across tokens) 03:19 - Encoder/decoder vs GPT decoder-only 03:26 - Resources to learn Q/K/V math + deeper dives Whether you're a hobbyist or a professional looking to get a grasp on GenAI Product Management, feel free to join our AI PM community for more such sessions   Fill out this form to receive an invitation to all my Free Live Sessions & Get Free AI Learning Resources: https://forms.gle/sRNjXnsurNxNAUQW7   Follow our LinkedIn Community Page: https://www.linkedin.com/company/mahesh-ai-pm-community/   Follow our Substack Page: https://substack.com/@myaicommunity   🔗 Check out my Cohort on Maven if you're looking to fastrack your AI PM Journey https://maven.com/mahesh-yadav/genaipm   Don't forget to like, subscribe, and hit the bell icon to stay updated with our latest videos!   #AIPM #ProductManagement #TechInterviews #AIJobs #MaheshYadav #ProductManager #GenAI #AIAgents #CareerPrep #InterviewTips #VibeCoding