About
The coding PM's guide to building AI products - where technical depth meets product strategy.
The Gap We're Filling
The AI product management landscape has a massive blind spot. While 70% of content focuses on high-level strategy and buzzwords, only 10% addresses the technical implementation details that actually matter when building AI products. This leaves PMs struggling with critical questions like:
- How do I architect prompts that work reliably in production?
- When should I prototype with code vs. directing engineers?
- How do I identify viable AI use cases beyond the obvious ones?
- What does non-deterministic UX design actually look like?
Meanwhile, top AI companies are offering $200K-$300K+ salaries for PMs with deep technical AI fluency - but where do you learn these skills?
What Makes This Different
AI Product Strategist bridges the gap between AI theory and production reality. This isn't another strategy blog filled with high-level frameworks. It's hands-on, technical content for PMs who want to understand the implementation details, not just wave their hands at them.
You'll find:
Technical Implementation Guides - Step-by-step coding tutorials, prompt engineering deep-dives, and vibe coding prototypes you can build yourself
AI Product Integration Strategies - Systematic frameworks for identifying AI opportunities, journey mapping for non-deterministic systems, and real-world implementation approaches
Production AI Management - The messy reality of scaling AI products, cost optimization, monitoring challenges, and everything they don't tell you in the demos
Practical PM Tools & Resources - Open-source projects, templates, automation scripts, and shareable resources that solve actual PM problems
Who This Is For
Primary audience: Technical PMs at AI companies who want deeper implementation knowledge and hands-on skills
Secondary audience: Traditional PMs transitioning to AI roles who need technical upskilling without getting lost in academic papers
Tertiary audience: Senior PMs at top tech companies evaluating AI opportunities and building technical fluency
My Approach
I believe the best AI PMs are technically fluent enough to prototype, evaluate implementation feasibility, and have substantive technical conversations with their engineering teams. This blog teaches those skills through practical projects, real code examples, and frameworks you can apply immediately.
Whether you're prompt engineering your way through a prototype, architecting an AI feature integration, or trying to make sense of production AI costs, you'll find actionable guidance that goes beyond surface-level strategy.