Day 1 — Orientation, AI Basics + Discovery

Focus: Orientation & AI Fluency (2 sections), Market Research & Synthesis, Requirements Generation

Section 1 — Orientation + GenAI Foundations for PMs

• What this class is – and isn’t. A brief overview of the AI landscape and how generative AI is reshaping product management.
• The PM productivity gap and where AI fits (brief framing, not a lecture)
• What GenAI actually is, in plain language: AI / ML / generative AI / LLMs, and what LLMs do (and don’t) — language engines, not search engines
• Capabilities and limits: where GenAI excels, where it falls flat, and the gray zone
• The five PM capability patterns — summarize, analyze, create, predict, recommend — with a FinServ-onboarding example for each
• Practice: “capability” drill — score a set of product-delivery tasks for PM-usefulness, and spot the one that’s a bad fit for AI

Section 2 — Prompting Like a PM

• How the major tools differ — Claude, ChatGPT, Gemini, and Copilot on the same task, and when to reach for which
• The five-element prompt anatomy: role, context, task, format, and constraints
• The iteration loop — refining a prompt one element at a time instead of starting over
• Holding context across a working session, plus persistent setups like Claude Projects and custom GPTs
• Enterprise guardrails: what’s safe to put into a public LLM — PII, IP, confidential roadmaps, and the rules that govern them
• Practice: take a crude prompt to executive-grade in four passes, and add your best to a reusable prompt library

Section 3 — Market Research & Synthesis

• Where AI fits across the seven product pillars — a quick orientation before the hands-on work
• AI-powered competitive and category research using deep-research tools
• Synthesizing customer interviews into themes, jobs-to-be-done, and pain points
• The observation-vs-insight line: AI surfaces observations fast, but the insight stays human
• Spotting hallucinations and building a verification habit you can trust
• Practice: turn three raw customer interviews into a synthesized themes-and-insights brief

Section 4 — Requirements Generation

• The discovery-to-PRD chain: how raw inputs become structured requirements
• From meeting notes to user stories, acceptance criteria, and edge cases
• Pressure-testing requirements against enterprise PRD standards
• The “red-team my requirements” pattern — using AI to surface what you missed
• Practice: turn a set of meeting notes into review-ready user stories and acceptance criteria, then capture the one workflow you’ll try on real work tomorrow

Day 2 — Strategy, Building + Analyzing

Focus: Strategy & Decision Support, Data Analysis & Metrics, Prototyping & Concept Validation

Section 5 — Strategy & Decision Support

• AI as thinking partner vs. AI as scribe — and when each earns its place
• Prioritization with AI: running RICE and trade-off analysis, then defending your call
• The “skeptical CEO” stress test — using AI to red-team your own strategy
• Pre-mortems on demand: surfacing failure modes before you commit
• Practice: prioritize a backlog under real constraints and produce a one-page opportunity brief for your own product

Section 6 — Data Analysis & Metrics

• Exploring product usage data with AI: finding patterns, anomalies, and segments
• SQL without the syntax — describing a question in plain English and refining the query
• Reading an A/B test honestly: drafting a readout that names its caveats
• Building a metrics tree with AI as your collaborator
• The critical-thinking check: catching the subtle error in AI-generated analysis
• Practice: turn a raw usage dataset into a findings readout, then pressure-test an AI analysis for errors

Section 7 — Prototyping & Concept Validation

• The shift from describing wireframes to generating working prototypes
• Turning a single prompt into an interactive prototype in Claude, ChatGPT Canvas, or Gemini Canvas
• Refining a concept in plain language — “make the empty state more helpful,” “add a confirmation step”
• Practice: build a clickable prototype for one feature of your product, from prompt to working concept

Section 8 — Prototyping continued + user research integration

• Heuristic review on demand: getting AI to critique an existing mockup
• Generating and refining customer journey maps
• Sharing work in progress and learning from the room
• Practice: produce an annotated design critique and a journey map, then transfer one of today’s workflows to your personal project

Day 3 — Communicating, Automating, Governance + Capstone

Focus: Stakeholder Communication, Workflow Automation & Agents, Governance, end-to-end capstone + Personal AI Roadmap

Section 9 — Stakeholder Communication + AI Governance

• The translation problem: one message, many audiences
• Turning a feature list into a roadmap narrative that lands
• Rehearsing difficult conversations — pushback from engineering or sales — with AI as a sparring partner
• Governance and ethics for enterprise PMs: data classification and retention, bias, and stakeholder transparency
• Practice: turn one product update into four audience-ready artifacts — exec summary, eng kickoff, customer announcement, and board bullet — then run an ethics audit on your own work

Section 10 — Workflow Automation & Agents

• The agentic shift, in plain terms: from “AI writes for me” to “AI works for me”
• Connector-based workflows that read your shared drives and tools
• Ready-to-steal patterns: weekly status synthesizer, backlog deduper, release-note generator, meeting-prep agent
• Practice: build a reusable PM assistant — a custom project, Gem, or GPT — loaded with your own context

Section 11 — Capstone Sprint (full framework, end-to-end)

• A fresh executive-sponsor ask, dropped in live
• The full framework end-to-end on your own product: research → requirements → prototype → metrics plan → exec summary
• Peer consulting: a built-in panel of PMs sharpening each other’s work
• Practice: run the complete GenAI Product Framework on your personal project in a single focused sprint

Section 12 — Showcase, Critical Use + Personal AI Roadmap

• Capstone showcase: see what the cohort built
• When NOT to use AI: hallucination patterns, judgment calls, IP and compliance traps, and stakeholder trust
• The PM’s evolving skillset — what to double down on, and what to hand to AI
• Resources, community, and follow-up office hours
• Practice: finalize your personal 30/90-day AI roadmap, including the one workflow you’ll deploy in week one