AI Product Metrics That Actually Matter: Beyond Accuracy
The most common product review question I get in AI initiatives is some version of: "What's the model accuracy?" It'
RAG vs Fine-Tuning vs Context Windows: Which Architecture Actually Works for Clinical AI?
Healthcare AI teams face a critical architecture decision: RAG, fine-tuning, or massive context windows? After shipping systems across clinical trials, drug discovery, and patient recruitment, here is what actually works in production.
Why Most AI POCs Fail: 7 Patterns I've Seen Across Industries
I have watched more AI POCs fail than succeed across healthcare, CPG, edtech, and high-tech. The same seven patterns show up every time. Here they are — with the fix for each one.
First Principles Thinking for Life Sciences Product Managers: Beyond Best Practices
Every PM framework - RICE, JTBD, Design Thinking - was built for consumer or enterprise SaaS. In life sciences, the biggest risks are regulatory rejection
First Principles of Building AI Products: What Most PMs Get Wrong
Building AI products requires breaking almost every mental model that made you a good PM for traditional software. Here are the first-principles errors I see most often — drawn from years of building across healthcare, edtech, and CPG.
RAG vs. Fine-Tuning vs. Prompt Engineering: A Life Sciences Product Manager's Decision Framework
Three techniques. One wrong choice can cost you six months and $500K. Here is the framework I use to pick the right approach for every life sciences AI use case.