Articles
Mostly about building products, healthcare tech, and lessons learned along the way.
Mostly about building products, healthcare tech, and lessons learned along the way.
4 articles on evals.

Most teams asking for AI don't have a model problem. They have a "which problem is even worth it" problem. Here's what a real AI audit surfaces — and why "don't build this" is often the most valuable line in the report.

Updated for 2026: lessons from two years using Claude Code in production. Context engineering, real eval frameworks, model economics, and agent workflows. What works.

The same failure pattern shows up everywhere: teams shipping fast and iterating on vibes instead of building systematic evaluation. Evals aren't a nice-to-have. They're the core competency of any serious AI product team.

LLM-as-judge evaluators feel like quality assurance but behave like rubber stamps. They fail hardest on the outputs that matter most: edge cases, safety-critical errors, domain-specific nuance. What to do instead.