What you’ll learn in this episode:

• The difference between an AI-powered product manager and an AI product manager


• Why prompt engineering for a product is different from prompting ChatGPT for personal use


• The role of prompt decomposition and orchestration in building robust AI features


• How to think about system design, risk mitigation, and cross-functional collaboration


• Why observability and logging traces are critical for LLM products


• The challenge of evaluating non-deterministic AI features (and why “thumbs up/thumbs down” isn’t enough)


• How to decide when AI is the right solution for a customer problem


• The hidden cost of ongoing maintenance for AI features

Resources & Links:

• Follow Teresa Torres: https://ProductTalk.org


• Follow Petra Wille: https://Petra-Wille.com

Mentioned in this episode:

• Behind the Scenes: Building the Product Talk Interview Coach by Teresa


• How I Designed & Implemented Evals for Product Talk’s Interview Coach by Teresa


• Leading Through the AI Wave—With Clarity, Not Confusion by Petra Wille


• AI Prototyping - All Things Product with Teresa & Petra episode


• ChatGPT


• Aman Khan — distinction between AI-powered vs. AI product managers


• The rise of Cursor: The $300M ARR AI tool that engineers can’t stop using | Michael Truell (co-founder and CEO) - Lenny’s Newsletter


• Devin AI and Windsurf (acquired by Cognizant)


• Yelp


• Uber


• Large language model (LLM)


• Claude


• Gemini


• BlackBerry


• Replit


• Zapier


• Amazon Web Server (AWS) Step Functions


• AI Agents in Practice: How Henrik Kniberg Sees the Future of Collaborative Work - Henrik Kniberg’s “interns” metaphor from Product at Heart 2025


• Anthropic


• Extensible Markup Language (XML)


• OpenAI


• Markdown


• Eval (short for evaluate)


• Personal information or personally identifiable information (PII)


• Machine learning (ML)