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12 Essential Frameworks Every AI Product Manager Needs

Episode 1 of the Crafting Product Excellence podcast, hosted by Torsten Feld, titled "12 Essential Frameworks Every AI Product Manager Needs" was published on April 6, 2026 and runs 22 minutes.

April 6, 2026 ·22m · Crafting Product Excellence

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What separates a traditional product manager from an AI product manager? Frameworks.In this episode, we break down the 12 essential thinking tools that every AI PM needs in their toolkit — from evaluating whether AI is even the right solution, to navigating the build-vs-buy decision, to designing human-in-the-loop experiences that actually work.Here's what we cover:• AI Opportunity Assessment — How to evaluate whether a problem is worth solving with AI, and when simpler solutions win• Build vs Buy vs API — The decision framework for choosing between building your own model, buying a solution, or calling an API• Data Flywheel Evaluation — How companies like Spotify and Uber create self-reinforcing loops where more users mean better AI• ML Product Metrics — Going beyond accuracy to measure what actually matters for your product and your users• Precision-Recall Product Tradeoff — Why you can't have it all, and how to choose the right error profile for your use case• RAG vs Fine-Tuning Decision — When to augment a foundation model with retrieval vs. when to train your own• GenAI Product Evaluation — A structured approach to assessing generative AI features before committing resources• GenAI Cost-Quality-Speed Tradeoff — The three-way tension every GenAI product team faces, and how to navigate it• Human-in-the-Loop Design — Designing AI systems where humans stay in control without killing the user experience• AI UX Testing Framework — How to test AI-powered interfaces when outputs are non-deterministic• Responsible AI Checklist — The ethical and safety considerations that should be part of every AI product review• Cross-Functional AI Collaboration — How to align engineering, data science, design, and business stakeholders on AI initiativesWhether you're transitioning from traditional PM to AI PM, or preparing for AI product management interviews at top tech companies, these frameworks give you a structured way to think through the unique challenges AI products present.

What separates a traditional product manager from an AI product manager? Frameworks.In this episode, we break down the 12 essential thinking tools that every AI PM needs in their toolkit — from evaluating whether AI is even the right solution, to navigating the build-vs-buy decision, to designing human-in-the-loop experiences that actually work.Here's what we cover:• AI Opportunity Assessment — How to evaluate whether a problem is worth solving with AI, and when simpler solutions win• Build vs Buy vs API — The decision framework for choosing between building your own model, buying a solution, or calling an API• Data Flywheel Evaluation — How companies like Spotify and Uber create self-reinforcing loops where more users mean better AI• ML Product Metrics — Going beyond accuracy to measure what actually matters for your product and your users• Precision-Recall Product Tradeoff — Why you can't have it all, and how to choose the right error profile for your use case• RAG vs Fine-Tuning Decision — When to augment a foundation model with retrieval vs. when to train your own• GenAI Product Evaluation — A structured approach to assessing generative AI features before committing resources• GenAI Cost-Quality-Speed Tradeoff — The three-way tension every GenAI product team faces, and how to navigate it• Human-in-the-Loop Design — Designing AI systems where humans stay in control without killing the user experience• AI UX Testing Framework — How to test AI-powered interfaces when outputs are non-deterministic• Responsible AI Checklist — The ethical and safety considerations that should be part of every AI product review• Cross-Functional AI Collaboration — How to align engineering, data science, design, and business stakeholders on AI initiativesWhether you're transitioning from traditional PM to AI PM, or preparing for AI product management interviews at top tech companies, these frameworks give you a structured way to think through the unique challenges AI products present.

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