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AI-Assisted Engineering

AI Code Review Has a Correctness Problem

Over the past two years, AI code review has gone from novelty to something many developers use every day, and the feedback can be genuinely impressive. Ask a modern model to review a pull request and you'll often get thoughtful comments about readability, naming, architecture, test coverage — the kind of observations that would have taken a human reviewer considerably longer to make.

I've spent the past while benchmarking several leading models against real pull requests, and the longer I looked at the results, the more one observation kept surfacing.

The reviews were often excellent.

The software wasn't.

The Work Happens Before the Prompt

Every week I see another discussion about becoming better at AI.

People compare models. They trade prompt libraries. They debate whether Claude writes cleaner code than GPT, or whether a particular workflow saves a few more minutes.

Those conversations are useful, but after spending the past year working with AI on real software projects, I keep coming back to the same conclusion:

I don't think the biggest productivity gains come from prompting.

I think they happen long before the first prompt is ever written.

Why I'm Finally Starting This Blog

I've wanted to write for a long time. Not because I wanted to become a blogger, build an audience, or establish myself as a thought leader — quite the opposite, actually.

Over the past twenty-plus years, I've had the privilege of working on some fascinating engineering problems. I've learned from incredible mentors, worked alongside talented developers, made more mistakes than I can count, and slowly developed opinions about software architecture, engineering culture, leadership, DevOps, AI, and what it means to build systems that last.

I've always wanted to share those experiences.