*changes may apply
Program Overview
This Module shifts product leaders from abstract requirements to tactical builders. Participants master AI discovery, structure machine-readable requirements, and learn how to use terminal-driven AI code assistants to build fully functional validation prototypes.
Session 1: The AI PM Shift - Strategy, Org Change, and Opportunity Discovery
What does it mean to be a PM in the AI era, and how do we identify AI opportunities that create real business value?
Core Hands-on Competencies
- AI Product Discovery Workflows: Analyzing business operations to find workflows with high automation potential; mapping user pain points, step frequency, operational cost, liability risks, and decision complexity.
- AI Value Mapping: Isolating precisely where AI intervention creates measurable value (e.g., radical speed execution, quality enhancement, personal scaling, or unlocking entirely new capabilities).
- Human-in-the-Loop Architecture: Systematically identifying clear intervention boundaries where human review, manual approval, or verification checkpoints must be maintained.
Additional topics that will be shortly reviewed:
- The changing PM role, including what AI automates in product workflows, what it cannot replace (judgment, taste, accountability, and stakeholder leadership), and role evolution across design, engineering, data, legal, and security teams.
- AI organizational change, covering strategies to navigate team friction, align executive expectations, avoid the “AI demo trap,” and define clear engineering handoff lines when PMs prototype independently.
- AI strategy and defensibility, establishing why simply using a model wrapper is not a strategy, and analyzing sustainable competitive moats like data flywheels, proprietary context, and deep workflow lock-in.
Hands-on Workshop: Participants choose a real-world product opportunity and construct a strategic roadmap mapping the target user, legacy workflows, exact AI intervention points, human-in-the-loop dependencies, success telemetry, and key deployment risks.
Session Deliverable: AI Opportunity Brief
Session 2: AI Product Discovery, Design, and AI-Native PRDs
How do we turn an AI opportunity into a clear product concept, testable PRD, and prototype plan?
Core Hands-on Competencies
- AI Capability Scoping: Selecting optimal product surfaces using Opportunity Solution Trees and Jobs-to-be-Done frameworks, scoping features as assistants, co-pilots, autonomous agents, or discrete background automations.
- AI-Native PRD Engineering: Structuring system requirements designed to be parsed equally by human engineers and AI coding agents. This includes defining explicit model behavior requirements, input/output data expectations, comprehensive edge-case boundaries, privacy constraints, and evaluation expectations.
Additional topics that will be shortly reviewed:
- Design decisions for AI user experiences, including evaluating when to automate processes silently vs. when to require user confirmation, displaying model confidence metrics, exposing system reasoning parameters, and implementing native user recovery controls (undo, edit, retry, and escalation paths).
Hands-on Workshop: Participants use Claude to parse raw qualitative user feedback logs, map out a high-leverage product feature, and write a comprehensive, machine-readable AI-Native PRD optimized for code generation.
Session Deliverable: AI-Native PRD + Prototype Scope Document.
Session 3: Rapid Prototyping with Claude Code I - Environment & Code Architecture
How can PMs use Claude Code to translate an AI-native PRD into a functional application structure without generating engineering debt?
Core Hands-on Competencies
- Terminal Environment Configuration: Navigating and configuring a local developer workspace, initializing project roots, and launching the Claude Code terminal engine.
- PRD-to-Code Mapping: Prompting Claude Code to interpret written PRD criteria and programmatically translate requirements into an application file structure and a clean layout blueprint.
- AI Code-Generation Scripting: Structuring prompt hierarchies to generate clean frontend application frames, layout grids, and interactive view wrappers built directly from Figma components or conceptual briefs.
Additional topics that will be shortly reviewed:
- The PM builder mindset, defining clear boundaries regarding what a product manager should build independently vs. what must remain strictly under engineering ownership, understanding prototype vs. production quality parameters, and avoiding organizational friction.
Hands-on Workshop: Participants configure their local workspace terminal, initialize a live repository via Claude Code, and programmatically render an interactive, navigation-ready frontend user interface application container.
Session Deliverable: Initialized Application Workspace & Interactive UI Shell
Session 4: Rapid Prototyping with Claude Code II - Backend Logic & Behavioral Testing
How do we inject mock intelligent behaviors, capture analytical interactions, and gracefully handle edge cases within the validation prototype?
Core Hands-on Competencies
- Mock Behavioral Injection: Writing and embedding fake backend logic, designing mock APIs, and configuring simulated real-time model behaviors or text-streaming simulations.
- Edge Case Implementation: Directing Claude Code to handle interactive failure states, programmatic error messages, and system fallback routines directly inside the prototype application.
- AI Code Refactoring & Auditing: Using Claude Code to systematically inspect, debug, and optimize generated files; mapping out product analytics logs to capture user behavioral events; compiling a clean developer handoff package.
Additional topics that will be shortly reviewed:
- Using real user validation data from prototype interaction testing to refine and adjust the primary PRD before production engineering cycles begin.
Hands-on Workshop: Participants wire up simulated backend behaviors, embed realistic model output flows, handle interface errors, and generate an automated developer-ready README file detailing system states.
Session Deliverable: Functional AI Product Prototype (Frontend UI, Mock Model Responses, Error Flows, Event Logging Plan, and Handoff Documentation)
Session Deliverable: Initialized Application Workspace & Interactive UI Shell
What Participants Graduate With
Participants exit this program equipped with a functional portfolio of production-ready blueprints. They will know how to utilize Claude and Claude Code to accelerate discovery, generate machine-readable PRDs, and construct functional interface prototypes.
More importantly, they will leave possessing the core competencies needed to confidently handle the complex, hidden vectors that determine whether an enterprise AI application can scale sustainably: establishing rigorous evaluation criteria, configuring safety guardrails, monitoring multi-step agent trajectories, and structuring optimized unit economic models to guarantee long-term profitability.