Awesome Ai Pm

Awesome AI PM

[](https://github.com/aakashg/awesome-ai-pm/stargazers) [](LICENSE) [](https://github.com/aakashg/awesome-ai-pm/commits/main)

A curated guide to AI product management. Frameworks, tools, skills, and resources for PMs building AI products.

Built and maintained by [Aakash Gupta](https://www.aakashg.com) | [Product Growth Newsletter](https://www.news.aakashg.com)

---

Table of Contents

• AI PM Fundamentals
• Core Technical Concepts
• AI Product Discovery
• AI Product Design
• AI Pricing
• AI Metrics and Evaluation
• AI Agents and Tools
• Prototyping with AI
• PM Workflows with AI
• AI PM Career
• AI PM Interview Prep
• Essential Reading
• Tools and Platforms

---

AI PM Fundamentals

What makes AI product management different from traditional PM work.

**The core difference:** Traditional products have deterministic behavior — the same input always produces the same output. AI products are probabilistic — the same input can produce different outputs. This changes how you spec, test, launch, and measure everything.

Key Concepts

• Probabilistic vs deterministic products — AI outputs vary. Your spec needs to define acceptable ranges, not exact outputs.
• Data as a product input — The quality of your training data directly determines product quality. PMs own the data strategy.
• Evaluation-driven development — You can't manually QA an AI feature. You need automated