This is one of the most dangerous moves you can make as an engineer:

Letting AI rewrite your legacy system.

In this episode, we confront that risk head-on.

Because if you’ve ever tried something like
“Claude, clean up this code”
you already know what happens next…

You get beautifully structured, modern code—
that completely breaks your production environment.

So how do you actually do this safely?

We walk through a battle-tested framework used in real engineering environments.

And it starts with a surprising rule:

Do not refactor first.

Instead, you force Claude to write characterization tests—capturing exactly how your messy, fragile, legacy code behaves today. Before you change anything, you lock reality in place.

From there, we build strict guardrails:

Use hierarchical CLAUDE.md to constrain behavior and decisions
Force an incremental loop: small change → run tests → verify
Never allow uncontrolled, large-scale rewrites

This is how you turn AI from a reckless optimizer into a disciplined engineer.

But even then, you’re not done.

Because the most dangerous bugs are the ones that look correct.

We dive into how to review AI-generated Pull Requests like a professional:

Catch hallucinated APIs that don’t exist
Identify subtle logic breaks that pass tests
Spot real security risks like SQL injection vulnerabilities

This episode isn’t about using AI faster.

It’s about using AI without breaking everything you’ve built.

If you want the full system for working with AI in real-world codebases—from safe refactoring to scalable workflows—
it’s all laid out in the book:

👉 https://www.amazon.com/dp/B0GQVHJRGB

Because the future isn’t AI replacing engineers.
It’s engineers who know how to control AI.