AI Product Roadmap Prioritization: Beyond RICE
RICE works fine when you know what you're building. AI products break that assumption constantly. Here's how I've adapted roadmap prioritization for the realities of probabilistic outputs, model drift, and shifting accuracy benchmarks.
Transformer Architectures Explained for Product People
Transformers are the engine inside every large language model you're building on. Understanding how they actually work changes how you scope features, set expectations, and make architectural decisions.
AI Product Manager Interview Guide: 50 Questions You'll Actually Get Asked
50 real AI PM interview questions organized by category — product sense, technical depth, AI/ML specific, stakeholder management, and case studies. With frameworks for answering each one.
Agentic AI Explained: What It Is, Why It Matters, and How to Build With It
If 2023 was the year everyone discovered LLMs could write surprisingly good text, and 2024 was the year enterprises started asking "but how do
Claude vs GPT-4 vs Gemini: Enterprise AI Platform Showdown
I've spent the last two years building AI products on top of Claude, GPT-4, and Gemini — across healthcare, edtech, and enterprise technology. Here's what I actually learned about when each one wins.
First Principles for Product Managers
Not the Elon Musk cliche - a practical explanation of first-principles thinking and how to actually use it on real product decisions.
AWS Bedrock vs Azure OpenAI vs Google Vertex AI
If you're an enterprise AI product leader, this is the decision that shapes your entire AI infrastructure for the next 3-5 years. I've evaluated all three platforms for production deployments and I'll give you the framework for making this call — not the one that wins on feature checklists, but the
Stakeholder Alignment Canvas for AI Products
AI products fail at the stakeholder layer more often than the technical layer. This canvas helps you map fears, hopes, and evidence needs for every stakeholder.
LLM Evaluation Frameworks: Beyond Benchmarks
A model that scores 90% on MMLU can fail 40% of your production queries. Benchmark performance and real-world performance are weakly correlated. Here is how to build evaluations that actually tell you whether your system is working.
Building in Public: How I Automated My Entire Content Pipeline with AI Agents
I built a system of AI agents that handles ideation, scripting, editing, and multi-platform publishing. Here's the architecture and what I learned.
Launch Playbook: Taking an AI Product from Beta to GA
The beta-to-GA transition for AI products is more complex than traditional software. You're not just releasing more broadly - you're accepting that your model will encounter new data distributions, new user behaviors, and new failure modes at scale. Here's the playbook I use.
Healthcare AI Product Roadmap: How I Prioritize Features Across 10 Clinical Products
I manage AI features across 10 clinical products simultaneously. Here is the prioritization methodology I have developed for regulated, multi-stakeholder healthcare environments.
Vercel AI SDK vs LangChain vs LlamaIndex for Production Apps
I've shipped production AI applications using all three of these frameworks. The choice matters more than most people think — the wrong framework can cost you weeks of refactoring when requirements change. Here's how to think about it.
The 10x Cost of Wrong AI Use Cases
A failed AI project doesn't just cost the budget allocated to it. It costs the organizational trust needed to fund the next one. Failed AI creates scar tissue — skepticism, risk aversion, and the response that kills good ideas before they start.
How I Use AI Agents to Run My Entire Content Operation
I'm an AI product manager who got tired of the gap between knowing how to build AI systems and actually using them in my own work. Here's how I built Jarvis — an agentic content operation that handles drafting, publishing, and distribution while I focus on ideas and strategy.