AI made code cheap. That sounds like good news, until your team becomes 10x faster at building the wrong thing.
This session is about the judgment layer that must come before AI-assisted building. We’ll walk through practical product-engineering frameworks for defining the problem well first: customer jobs, problem trees, evidence quality, success metrics, scope boundaries, and the “should we build this?” decision.
The goal is not to turn engineers into product managers. It is to help engineers become sharper owners of product outcomes. Before you start prompting, or generate a prototype, you need to know who the customer is, what pain is real, what behavior should change, and how you will know the work mattered.