Not the idealized version. The actual one.

I lead a distributed engineering team at Kaz Software. We ship across multiple projects simultaneously, which means PR queues pile up fast. Two years ago, every review was entirely human. Today, roughly 60% of the first-pass review work is handled by AI before a human engineer ever opens the diff.

This is not a post about what AI could do for your review workflow. This is a walkthrough of what I actually do, the tools I rely on, and where I still refuse to let AI make the call.

Three tools do almost all the work:

GitHub Copilot handles in-editor, inline review and the PR summary layer directly inside GitHub.

Claude Code handles deep architectural review, spec alignment checks, and the pre-commit audit that I run before any significant PR goes up.

Codex CLI handles the command-line tasks: automated diff analysis, generating test scaffolding for uncovered paths, and batch processing when I need to review multiple files with a specific lens.

I spent my first 6 years as an engineer debugging without Claude Code or Codex, reading docs that hadn't been summarised by AI, and struggling through concepts until they stuck.

That struggle built something AI can't replace: judgement.

Now I use AI every day. And it's the best thing that's happened to my productivity. But it only works because the fundamentals came first.

I can review AI-generated code because I understand system design. I can spot bad architecture because I've built and operated real systems. I can tell when the output looks confident but is completely wrong because I've been wrong enough times myself.

Pre-AI fundamentals plus post-AI speed. That's the combo.

AI makes fast engineers faster. But it also makes uninformed engineers more dangerous.

Don't skip the fundamentals to chase the tools. Learn both. But learn them in the right order.

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♻️ Repost to inspire another engineer to learn the fundamentals
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