Teresa Torres' Step-by-Step Guide to AI Product Discovery
Product Growth Podcast
Navigating AI Product Discovery
This chapter explores the unique challenges and strategies in AI product management, highlighting the differences from traditional approaches. It emphasizes the significance of context engineering, orchestration, and error analysis in developing effective AI features like an interview coaching tool. The speaker shares practical insights and personal experiences, showcasing the dynamic nature of product management in the AI landscape.
In today’s episode, we have one of the two voices I wanted most when I started this podcast: Teresa Torres. Alongside Marty Cagan, she was in my top guests to have.
That’s because she has trained over 17,000 PMs in 100 countries.
And in today’s episode, she’s breaking down one of the most important elements of PMing: discovery.
She gives a masterclass on how to use the learnings from her smash hit book Continuous Discovery Habits for the AI age, covering both:
1. How to do discovery for non-AI features with AI tools
2. How to discovery for AI features
If you’ve ever wondered why your product ideas sometimes flop, even when the interviews and research looked promising… you’re about to find out why!
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Brought to you by:
Miro: The innovation workspace
Jira Product Discovery: Build the right thing
Parlance Labs: Practical consulting that improves your AI
Product Faculty: #1 AI PM Certification (Class Starts: 15 Sep, get $500 off)
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Timestamps:
Teresa's Background - 0:00
Story-Based Interviewing - 3:20
Fake Discovery Signs - 4:08
Assumption Testing - 4:39
Continuous Discovery Framework - 5:35
AI Changes Discovery - 8:01
AI Synthesis Concerns - 9:21
AI Prototyping Era - 12:45
Ads - 15:45
AI Prototyping Workflow - 17:32
Common Interview Mistakes - 22:24
Interview Synthesis - 24:26
OST Updates - 28:53
Discovery Theater - 30:52
Ads - 32:15
Real Product Management - 34:03
AI Product Discovery - 35:29
Context Engineering - 39:16
Orchestration Explained - 42:03
Error Analysis - 46:01
Observability & Traces - 46:05
Claude Code Demo - 49:15
Business Numbers - 52:56
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Key Takeaways:
Takeaways:
01. Stop Shipping Blind. Your AI product isn't truly valuable until you validate it. Go beyond just building; understand user needs deeply with personas, journey maps, and jobs-to-be-done.
02. MOM Test = Your Secret Weapon. The "MOM Test" is about asking questions that even your most supportive friend can't lie about. Don't ask if users "would" use your AI. Ask about their past behaviors and real problems. This helps you define success metrics and avoid building a fancy toy nobody needs.
03. Evaluate Everything, Relentlessly. AI Evals are not just a technical task for engineers, but the most critical tool for Product Managers to build high-quality, trustworthy AI products. Use them to understand, refine, and continuously improve your AI.
04. Passion Won't Land the Job. Proof Will. "I'm passionate"...great I guess, but recruiters want to see what you've done. Your portfolio is your direct line to showing you can actually do the job.
05. Build Your AI Portfolio. Now. Don't wait for experience. Create product teardowns of AI tools, develop case studies, or launch small side projects. This is your living, breathing proof of thinking and skill.
06. Forget the Resume. Add Value. The ultimate job hack? Identify a problem at a target company and propose a solution, or even build a prototype before you apply. This showcases initiative and concrete skills.
07. You’re At Fault (Brutal, I Know). Nailing Prompt Engineering is a direct path to better AI outputs. If your AI misbehaves, it's often your fault for unclear instructions. Refine your prompts for smarter, more reliable AI.
08. Generic Resumes In The Bin! Forget sending generic resumes into the void. There are three distinct approaches: just a resume, adding a portfolio and cover letter, or the ultimate "Value Add" where you solve a company's problem before applying.
09. AI Will Do Your Dishes (Metaphorically). While AI Agents promise incredible autonomy and action, remember they still need clear goals and defined tasks. So, while your AI PM dream is big, maybe don't expect it to clean your dishes (yet) – stick to email automation for now!
10. Don't Trust LLMs Blindly. LLMs are powerful. But they need continuous human validation and evaluation frameworks. Automate grading where possible, but always, always, have a human in the loop for critical judgment.
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Check out the conversation on Apple or Spotify and the demo on YouTube.
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Related Podcasts:
AI Product Discovery: Complete Course
How to Do Product Discovery Right with Pawel Huryn
Marty Cagan on the 4 Key Risks and Importance of Discovery
How to Survey and Learn From Your Users with George Harter
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P.S. More than 85% of you aren't subscribed yet. If you can subscribe on YouTube, follow on Apple & Spotify, my commitment to you is that we'll continue making this content better.
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