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AI-Assisted Engineering

Better Questions, Not Better Prompts

The advice for getting more out of AI has been remarkably consistent for a few years now. Be specific. Give it context. Define the role. State the constraints. Say how you want the answer formatted. All of that genuinely helps, and I still do most of it.

But when I look back at the sessions where an AI coding assistant was actually useful — where it found the real problem rather than a plausible one — almost none of them turned on the opening prompt. They turned on what I asked next.

I'm starting to think we've been naming the wrong skill.

From Prompt to Platform: Why Modern AI Code Review Needs an Architecture

When most people imagine an AI code reviewer, they picture a single prompt: hand the model a pull request, ask for a review, read what comes back. For small changes, that works surprisingly well — which is what makes the failure on larger ones interesting. As a pull request grows, the model starts losing context. Behavioral defects become harder to find. Security, correctness, architecture, and testing all compete for the same limited attention, and the review quietly becomes shallower even though the model hasn't changed.

I wrote recently about the correctness problem in AI code review, and ended with a suspicion: that the next generation of reviewers would be defined less by larger models than by harnesses built around them. This article is my attempt to describe what I think that harness looks like. Because the limitation I keep running into isn't model intelligence.

It's architecture.

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.