A comprehensive deep-dive into generative AI — built specifically for senior product managers at top B2C companies. No fluff, no code. Just the technical fluency you need to lead AI-powered products with confidence.
You're a senior PM at a top-tier B2C company. You've shipped products used by millions. You know discovery, specs, and backlogs — but generative AI has changed the rules. This course bridges the gap.
Leading product teams at companies like Google, Amazon, Meta, Uber, Netflix, or similar. You need to make architecture decisions with your engineers.
You're tasked with adding AI to your product — chatbots, recommendations, generative features — and need to understand what's actually possible.
You present to leadership and need to set realistic expectations about what AI can and can't do today — and where the technology is heading.
You work alongside ML engineers and data scientists and need the vocabulary and mental models to collaborate effectively.
Most AI content is either too high-level ("AI will change everything!") or too technical (research papers and code tutorials). This course sits in the critical middle — deep enough to be useful, accessible enough to finish.
RAG vs. fine-tuning? Single agent vs. multi-agent? Cloud API vs. self-hosted? You'll have the frameworks to decide.
Go beyond "add a chatbot." Learn anticipatory UX, progressive autonomy, learning loops, and the patterns that define great AI products.
Understand hallucination, context windows, reasoning limits, and cost — so you can promise what AI can actually deliver.
Learn evaluation frameworks, feedback loops, and continuous improvement strategies that compound over time — not just prompt engineering.
Understand safety, alignment, bias, guardrails, and human-in-the-loop oversight — critical for any PM shipping AI at scale.
Six sections, each building on the previous. By the end, you'll have a complete mental model — from how a single token is predicted, all the way up to how a fleet of specialized agents coordinates complex workflows.
The strategic foundation. Why generative AI is structurally different from the Internet, cloud, and mobile revolutions. How AI impacts the four types of product work. How your core PM responsibilities are being rewritten — and how customer expectations are permanently shifting.
The technical foundation — explained for PMs, not PhDs. How next-token prediction works, tokenization, the Transformer architecture, and the 7 critical limitations every PM must internalize: hallucination, knowledge cutoffs, context windows, reasoning failures, and more.
The four upgrade layers that transform a base LLM into a production-grade product. Deep dives into RAG (Naive → Enhanced → Modular), reasoning techniques (Chain of Thought → Tree of Thoughts), memory architectures, and tool integration patterns.
The AI improvement stack. How to evaluate AI quality rigorously, design feedback loops, decide when fine-tuning is worth it, and understand how RLHF and its successors create the alignment flywheel that separates great AI products from demos.
AI agents — the frontier of product development. The Chatbot → Copilot → Agent spectrum. The agent architecture loop: perception, planning, execution, and reflection. Goal definition, progressive autonomy, trust frameworks, and the safety guardrails needed for production.
The most advanced section. How to decompose complex tasks into multi-agent workflows. Communication protocols, orchestration patterns (sequential, parallel, hierarchical), resource management, conflict resolution, and monitoring at scale.
Every concept is grounded in how real companies are building AI products today. No hypotheticals — just battle-tested examples from the best in the industry.