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Free Download Transition from Product Manager to AI Product Manager
Published 5/2026
Created by Ramin Hoodeh
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 7 Lectures ( 4h 39m ) | Size: 9.74 GB
Learn the Next Generation of AI Product Management Skills and Get the Job
What you'll learn
Build the 5-Layer AI Product Stack: LLMs, prompt engineering, RAG, agents, MCP, evals, and guardrails - one durable career framework. Set up a personal AI Product OS using ChatGPT, Claude, or Gemini - loaded with your company context and connected to real tools. New AI Product Management values system, mindset and skillsets. Go from blank prompt to clickable AI prototype in one sitting using agentic workflows and the AI-Native Product Loop. Write and run professional LLM evaluation suites - functional evals, safety evals, and regression tests - on real AI features. Wire up AI guardrails, observability dashboards, cost controls, and fallback strategies for production-grade AI products. Master the AI PM interview: portfolio strategy, five behavioural stories, product sense frameworks, and a live answer structure. Understand how LLMs actually work - tokens, parameters, context windows, temperature, chain-of-thought - without writing a line of code. Learn to orchestrate AI agents, connect MCP servers, and automate PM workflows that used to take weeks.Requirements
No coding, machine learning, or data science background needed - built for product people, not engineers. Basic familiarity with Product Manager concepts: PRDs, user stories, roadmaps, and agile workflows. Recommended to do my Full Product Development Process course first, also provided for free in this course. A laptop and a free or paid account on ChatGPT, Claude, or Google Gemini. Setup is done live on camera.Description
This course is a a practical successor to the classic start-to-finish Product Innovation Process course (now also free with this course) - rebuilt for a world where the core material is probabilistic.
If you are..
Currently Product Manager looking to evolve into an AI Product Manger Looking to become a Product Manager but want the most updated overview of the role today An AI Builder looking to build upon your idea release a successful AI-native productThen this course is for you.
The context: the old pipeline worked - until the material changed
For decades, product teams ran a linear pipeline -Idea → Design → Build → Test → Ship - because the material was mostly deterministic. You could specify behavior, implement it, QA it, and trust it to behave.
AI changes the physics.
When the system's core behavior isprobabilistic, stage-gates stop protecting you
Product Requirement Documents can't fully specify "correct." Demos don't predict production. Model updates shift behavior underneath you. "Ship" becomes an ongoing relationship with a living system.The stable foundation: the 5-layer stack (how to think clearly)
Models change fast. Your operating system shouldn't.
This course uses a stable 5-layer stack so you can locate any AI product problem in the right place
Model - capability, latency, cost; what the base model can and can't doContext - prompts, RAG, memory, knowledge; what the model canseeOrchestration - agents, tools, workflows; how work gets done across stepsGovernance - evals, guardrails, observability; how quality stays safe and measurableHuman - vision, empathy, taste, communication, judgment; what cannot be delegatedThe new approach: replace the pipeline with a loop (what you do day-to-day)
Instead of pushing work through a line, you learn to run a tight loop
Talk (Human + Context)→ Decide (Human + Governance)→ Build (Model + Orchestration)→ Observe (Governance)→ Iterate (all layers)
You don't just learn concepts - you learn a repeatable way of working that holds up when output isn't guaranteed.
The course spine (the one argument you're learning)
Everything in this course is organized around one property and what it forces
Property: outputs are probabilistic, not deterministic.Four consequences You can't "prompt and hope" → you must understand the model and load context. Probabilistic systems require loops, not straight lines. Probabilistic systems require evals + guardrails, not hope. Probabilistic systems change what being a professional means.Each lesson "installs" one layer of the stack and explains one consequence for each (with Lesson 2 planting the full map).
The 6-lesson arc (what you'll learn, in order)
Lesson 1 - End of PM (and the Stack) Install: the full map (old line → new loop → 5-layer stack) Rule #1:Build something small this week.Lesson 2 - Installing the Model Layer Install: model literacy for product judgment (capability/latency/cost, updates, selection) Rule #2:Never confuse a Model Layer update with a Stack change.Lesson 3 - Installing the Context Layer Install: your owned context system (five context files) so the model acts like a teammate Rule #3:The model is rented. Your context is owned.Lesson 4 - Installing the Orchestration Layer Install: runTalk → Decide → Build → Observe → Iterate end-to-end on real PM work Rule #4:You are not the builder. You are the conductor.Lesson 5 - Installing the Governance Layer Install: eval suite + guardrails + observability so you can ship responsibly at speed Rule #5:Ship what you can measure. Hold what you cannot.Lesson 6 - Installing the Human Layer Install: the professional posture when execution is cheap (vision, empathy, taste, communication, judgment) Rule #6:You are the Context Layer.Who this course is for
Product managers hearing "AI-native," "agentic," and "LLM integration" in every meeting who want to lead those conversations, not just nod along. Aspiring PMs trying to land their first AI product manager role with a real portfolio and framework - not just another certificate. Founders and product leads shipping AI features (copilots, chatbots, AI agents, recommendations) who need evals and guardrails, not guesswork. Designers, engineers, or analysts switching into product management who want to arrive AI-fluent from day one. Senior PMs and product leaders who need a structured operating system for AI product development - not a prompt engineering tips playlist.Homepage
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