Builder Bootcamp: Become an AI-native PM

Uzma Barlaskar

Founder. Previous:Meta, WhatsApp Dir PM

Learn how to build production-ready AI prototypes

The role of the Product Manager is changing. Companies such as Meta, AirBnB, Coinbase, and AI-native companies such as Anthropic increasingly expect PMs to validate ideas by building and shipping an initial version themselves, collecting real user feedback and usage data before scaling with engineering resources.

Creating an AI demo is easy.

Building something users can actually use is much harder.

The difference between a prototype and a product isn't code generation. It requires rigor : clear specifications, good design decisions, testing, deployment, analytics, and knowing what to measure after launch.

In this hands-on workshop, you'll learn how to:

Turn ambiguous product ideas into AI-native specifications that coding agents can execute accurately.
Design and build production-ready applications using modern AI development tools.
Add testing, evals, and deployment workflows that make software reliable enough for real users.

By the end of the session, you'll have taken a real idea from concept to a live application and learned the AI-native product lifecycle now expected of modern product managers.

What you’ll learn

Learn the AI-Native PM workflow: spec, build, launch, and validate products with AI tools.

Define requirements, constraints, edge cases, and acceptance criteria that AI coding agents can execute accurately.
Structure specifications to minimize rework, reduce hallucinations, and accelerate development cycles.

Generate multiple UI and workflow concepts in parallel and evaluate them against user needs.
Learn how to identify and converge on the strongest product direction before committing engineering effort.

Use AI coding agents to implement features, review outputs, and manage iterative development.
Apply production best practices including how to ensure architecture consistency across UI, backend and database

Instrument applications with analytics and identify the metrics that guide iteration and product-market fit decisions.
Create tests and evals that validate product behavior and catch issues before release.

Workshop agenda

Specs for coding agents
Learn how to write AI-native product specifications that eliminate ambiguity, capture constraints and edge cases, and define "done" clearly enough for an AI coding agent to build accurately in the fir
Rapid Design Exploration
Generate multiple UI directions in parallel, pressure-test them against user goals, and quickly converge on a design direction worth building. Leave with a validated design approach.
Building with Agentic Coding Tools
Turn your idea into a functional prototype using AI coding agents while learning how to guide implementation, review output, and avoid common failure modes of AI-generated code.
Evals and Quality Gates
Learn how to define success criteria and create evals that continuously test product behavior and AI outputs. Leave with an evaluation framework that catches failures before your users do.
Deploying and Launching
Take your application from local prototype to publicly accessible product by learning deployment workflows, launch checklists, and how to decide what is ready to ship versus what can wait.