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Apr 10, 2026
Zero to Engineer: How to Actually Validate AI-Generated Code
Stop Shipping Code You Can't Explain (Where every Friday we discuss something new)
Happy Friday Friends!
Here’s a stat that stopped me mid-scroll this week: 84% of developers now use AI tools in their daily workflow. But only 29% trust the output.
That’s not a contradiction. That’s a skill gap.
The developers who trust AI aren’t naive. They’ve just learned how to validate what it gives them. And that’s exactly what I’m teaching you today.
What’s Inside
Why blindly accepting AI code will bite you
The 4-step validation framework I use on every AI-generated block
Real examples from projects I shipped this year
How to build the muscle that separates junior from senior
Also, be a kind Human and support the blog!
AI code looks clean. It runs. It even has nice comments. And that’s exactly why it’s dangerous for newer developers.
A study analyzing 153 million lines of changed code found that code duplication increased 4x when AI tools were involved. Pull requests with AI-generated code had 1.7x more issues than human-written code.
The code compiles. The code is wrong. Those two things can absolutely coexist.
When I’m building client projects at FabBuilds or working on my own side projects, I use AI to help speed up my workflow. But I never let it drive blind. Here’s the framework I use every single time.
The 4-Step Validation Framework
1. Read It Like You Wrote It
Before you even run it, read every line. Not skim. Read.
AI loves to add things you didn’t ask for. Extra state variables. Unnecessary API calls. Dependencies you don’t need. I’ve caught Claude Code importing entire libraries when three lines of vanilla code would’ve done the job.
Ask yourself: “If I wrote this from scratch, would I make these same choices?”
If the answer is no, that’s your first red flag.
2. Trace the Data Flow
Pick one piece of data, a user input, a database record, an API response, and trace it through the entire generated code. Where does it enter? Where does it get transformed? Where does it exit?
AI-generated code loves to create variables that go nowhere and transformations that cancel each other out. I once caught a block that fetched data, transformed it, stored the transformation, then fetched the same data again two functions later.
This step alone catches about 60% of the junk.
3. Break It On Purpose
This is where most people stop validating and I get it, the code works, why poke it?
Because your users will poke it.
Feed it empty strings. Pass null where it expects an object. Hit the submit button twice. Disconnect your internet mid-request. These aren’t edge cases. This is what could happen on a random Tuesday.
AI tools are trained on happy-path examples. They generate happy-path code. Your job is to be the unhappy path.
4. Question the Architecture
This is the senior-level move. The code works, you’ve validated the logic, it handles errors. Now zoom out.
Does this approach scale? Did it create tight coupling where loose coupling would’ve been smarter? Is it solving today’s problem in a way that creates tomorrow’s tech debt?
I’ve had AI suggest putting business logic inside UI components, storing sensitive data in AsyncStorage instead of SecureStore, and hardcoding values that should obviously be environment variables. Each one “worked.” Each one would’ve cost me hours later.
A Real Example
Last month I was building the paywall flow for an App I’m releasing. Claude Code generated a clean implementation that passed every test. But when I traced the data flow (step 2), I found it was checking subscription status on every single screen render instead of caching it in my AuthContext.
Functionally identical. Architecturally, a nightmare. That’s 50+ unnecessary RevenueCat API calls per session on a mobile app where every millisecond of battery life matters.
The AI didn’t know my architecture. It solved the problem it could see. My job was to validate against the system it couldn’t.
The Takeaway
AI isn’t the problem. Skipping validation is.
The developers in that 29% who trust AI didn’t get there by hoping for the best. They built a process. They treat AI output the same way they’d treat a pull request from a new junior developer: assume good intent, verify everything.
That’s the skill that separates “I use AI” from “I ship with AI.”
Start with these four steps on your next project. They’ll slow you down by ten minutes and save you ten hours.
Why You Should Subscribe
Every Friday, Zero to Engineer breaks down one concept that makes you a better developer. No fluff. No theory you’ll never use. Just the stuff I’m learning and applying while building real products.
Hit subscribe so you don’t miss next week’s breakdown.
Everything I teach here comes from building real products for real clients. If you need a site, funnel, or automation built right, that’s literally what I do at FabBuilds, starting at $500.
Let’s Build It Beautifully,
Fab
Here’s a stat that stopped me mid-scroll this week: 84% of developers now use AI tools in their daily workflow. But only 29% trust the output.
That’s not a contradiction. That’s a skill gap.
The developers who trust AI aren’t naive. They’ve just learned how to validate what it gives them. And that’s exactly what I’m teaching you today.
What’s Inside
Why blindly accepting AI code will bite you
The 4-step validation framework I use on every AI-generated block
Real examples from projects I shipped this year
How to build the muscle that separates junior from senior
Also, be a kind Human and support the blog!
AI code looks clean. It runs. It even has nice comments. And that’s exactly why it’s dangerous for newer developers.
A study analyzing 153 million lines of changed code found that code duplication increased 4x when AI tools were involved. Pull requests with AI-generated code had 1.7x more issues than human-written code.
The code compiles. The code is wrong. Those two things can absolutely coexist.
When I’m building client projects at FabBuilds or working on my own side projects, I use AI to help speed up my workflow. But I never let it drive blind. Here’s the framework I use every single time.
The 4-Step Validation Framework
1. Read It Like You Wrote It
Before you even run it, read every line. Not skim. Read.
AI loves to add things you didn’t ask for. Extra state variables. Unnecessary API calls. Dependencies you don’t need. I’ve caught Claude Code importing entire libraries when three lines of vanilla code would’ve done the job.
Ask yourself: “If I wrote this from scratch, would I make these same choices?”
If the answer is no, that’s your first red flag.
2. Trace the Data Flow
Pick one piece of data, a user input, a database record, an API response, and trace it through the entire generated code. Where does it enter? Where does it get transformed? Where does it exit?
AI-generated code loves to create variables that go nowhere and transformations that cancel each other out. I once caught a block that fetched data, transformed it, stored the transformation, then fetched the same data again two functions later.
This step alone catches about 60% of the junk.
3. Break It On Purpose
This is where most people stop validating and I get it, the code works, why poke it?
Because your users will poke it.
Feed it empty strings. Pass null where it expects an object. Hit the submit button twice. Disconnect your internet mid-request. These aren’t edge cases. This is what could happen on a random Tuesday.
AI tools are trained on happy-path examples. They generate happy-path code. Your job is to be the unhappy path.
4. Question the Architecture
This is the senior-level move. The code works, you’ve validated the logic, it handles errors. Now zoom out.
Does this approach scale? Did it create tight coupling where loose coupling would’ve been smarter? Is it solving today’s problem in a way that creates tomorrow’s tech debt?
I’ve had AI suggest putting business logic inside UI components, storing sensitive data in AsyncStorage instead of SecureStore, and hardcoding values that should obviously be environment variables. Each one “worked.” Each one would’ve cost me hours later.
A Real Example
Last month I was building the paywall flow for an App I’m releasing. Claude Code generated a clean implementation that passed every test. But when I traced the data flow (step 2), I found it was checking subscription status on every single screen render instead of caching it in my AuthContext.
Functionally identical. Architecturally, a nightmare. That’s 50+ unnecessary RevenueCat API calls per session on a mobile app where every millisecond of battery life matters.
The AI didn’t know my architecture. It solved the problem it could see. My job was to validate against the system it couldn’t.
The Takeaway
AI isn’t the problem. Skipping validation is.
The developers in that 29% who trust AI didn’t get there by hoping for the best. They built a process. They treat AI output the same way they’d treat a pull request from a new junior developer: assume good intent, verify everything.
That’s the skill that separates “I use AI” from “I ship with AI.”
Start with these four steps on your next project. They’ll slow you down by ten minutes and save you ten hours.
Why You Should Subscribe
Every Friday, Zero to Engineer breaks down one concept that makes you a better developer. No fluff. No theory you’ll never use. Just the stuff I’m learning and applying while building real products.
Hit subscribe so you don’t miss next week’s breakdown.
Everything I teach here comes from building real products for real clients. If you need a site, funnel, or automation built right, that’s literally what I do at FabBuilds, starting at $500.
Let’s Build It Beautifully,
Fab