The speed of execution is what makes AI great these days the ability to generate code or entire features that would normally take a full day,in a few minutes of prompting. That's incredible, in the grand scheme of things.
What differentiates a senior engineer from a junior one is the sheer willingness to dig deeper into code to review it, question it, and spot issues before it ever gets merged into production. But that instinct comes with experience. Senior developers have seen patterns before. They know how to properly handle exceptions, where systems tend to break, and what "clean" actually looks like versus what just looks clean.
Right now, AI has broken down the barrier to entry for software engineering. Folks without any coding background can spin up a real product using nothing but natural language and an LLM at the core. That's genuinely exciting. But what most people don't know and don't think to ask is whether what the AI built can actually scale, hold up under real usage, or survive contact with an edge case it wasn't shown.
I put together 5 red flags you need to look out for in AI-generated code. This is useful whether you're a mid-level developer leaning on AI to move faster, or a product owner shipping with AI as your primary engineering resource.
As a senior engineer reading this, i beleive all of the issue mentioned in this post should not be obscure to you.
LLMs hallucinate a lot especially when you don't give them full context of what you're building, or when you let them guess at the rest. You'll eventually see things like a random import sitting at the top of your file that doesn't actually do anything, or a function call to a method that doesn't exist in the package you're using, or an API endpoint the AI simply assumed was there.
It's important that you track all of this closely, to make sure your system stays consistent with the actual imports, packages, and API URLs you depend on not the ones the LLM confidently invented. A five-second check now saves you a broken build later, usually at the worst possible time.
How to catch it: Verify every import and API call against the actual documentation before you merge. Run a build or install step immediately after generation if a package doesn't exist, you want that to fail loudly right away, not three commits deep when it's tangled into everything else.
This one is sneakier, because the code often reads fine line by line. Each individual statement looks reasonable. It's only when you trace the logic end-to-end that you notice it doesn't actually hold together a variable gets reassigned in a way that quietly breaks an earlier assumption, a conditional branch that can never actually be reached, or two functions that handle the exact same case in two different, contradicting ways.
This happens because LLMs generate code the way they generate text one plausible piece at a time, without always holding the full picture in mind. It can produce something that's locally correct but globally broken, and that kind of bug is exactly the one that slips past a quick glance and shows up in production three weeks later.
How to catch it: Trace the logic manually, or step through it with a debugger, before you trust it with anything important. A trick that works well here: ask the AI to explain its own logic back to you, step by step. If the explanation doesn't hold up on its own, the code underneath it probably doesn't either.
AI-generated code has a habit of optimizing for "it works," not "it's safe." That usually means missing input validation, hardcoded secrets sitting right there in plain text, or unsafe defaults that quietly work fine in development and become a real liability the moment real users touch the system.
The dangerous part isn't that these gaps exist, it's that they're silent. Nothing throws an error. Nothing fails a build. The code runs perfectly, right up until someone finds the gap, and it's rarely going to be you who finds it first.
How to catch it: Make a security linter or scanner a standard part of your workflow, not an optional extra step. Never accept a hardcoded secret in code, no matter how "temporary" it's meant to be. And be deliberate about asking the AI directly: Does this handle untrusted input? What happens if this value is empty, malformed, or malicious?
The speed of execution is what makes AI great these days the ability to generate code or entire features that would normally take a full day,in a few minutes of prompting. That's incredible, in the grand scheme of things.
What differentiates a senior engineer from a junior one is the sheer willingness to dig deeper into code to review it, question it, and spot issues before it ever gets merged into production. But that instinct comes with experience. Senior developers have seen patterns before. They know how to properly handle exceptions, where systems tend to break, and what "clean" actually looks like versus what just looks clean.
Right now, AI has broken down the barrier to entry for software engineering. Folks without any coding background can spin up a real product using nothing but natural language and an LLM at the core. That's genuinely exciting. But what most people don't know and don't think to ask is whether what the AI built can actually scale, hold up under real usage, or survive contact with an edge case it wasn't shown.
I put together 5 red flags you need to look out for in AI-generated code. This is useful whether you're a mid-level developer leaning on AI to move faster, or a product owner shipping with AI as your primary engineering resource.
As a senior engineer reading this, i beleive all of the issue mentioned in this post should not be obscure to you.
LLMs hallucinate a lot especially when you don't give them full context of what you're building, or when you let them guess at the rest. You'll eventually see things like a random import sitting at the top of your file that doesn't actually do anything, or a function call to a method that doesn't exist in the package you're using, or an API endpoint the AI simply assumed was there.
It's important that you track all of this closely, to make sure your system stays consistent with the actual imports, packages, and API URLs you depend on not the ones the LLM confidently invented. A five-second check now saves you a broken build later, usually at the worst possible time.
How to catch it: Verify every import and API call against the actual documentation before you merge. Run a build or install step immediately after generation if a package doesn't exist, you want that to fail loudly right away, not three commits deep when it's tangled into everything else.
This one is sneakier, because the code often reads fine line by line. Each individual statement looks reasonable. It's only when you trace the logic end-to-end that you notice it doesn't actually hold together a variable gets reassigned in a way that quietly breaks an earlier assumption, a conditional branch that can never actually be reached, or two functions that handle the exact same case in two different, contradicting ways.
This happens because LLMs generate code the way they generate text one plausible piece at a time, without always holding the full picture in mind. It can produce something that's locally correct but globally broken, and that kind of bug is exactly the one that slips past a quick glance and shows up in production three weeks later.
How to catch it: Trace the logic manually, or step through it with a debugger, before you trust it with anything important. A trick that works well here: ask the AI to explain its own logic back to you, step by step. If the explanation doesn't hold up on its own, the code underneath it probably doesn't either.
AI-generated code has a habit of optimizing for "it works," not "it's safe." That usually means missing input validation, hardcoded secrets sitting right there in plain text, or unsafe defaults that quietly work fine in development and become a real liability the moment real users touch the system.
The dangerous part isn't that these gaps exist, it's that they're silent. Nothing throws an error. Nothing fails a build. The code runs perfectly, right up until someone finds the gap, and it's rarely going to be you who finds it first.
How to catch it: Make a security linter or scanner a standard part of your workflow, not an optional extra step. Never accept a hardcoded secret in code, no matter how "temporary" it's meant to be. And be deliberate about asking the AI directly: Does this handle untrusted input? What happens if this value is empty, malformed, or malicious?
A lot of AI-generated code ships with no tests at all, or with tests that only check the happy path the one scenario where every input is exactly what was expected and nothing goes wrong. That's the easiest case to test, and also the least useful one, because real usage is rarely that clean.
The danger here is that a green checkmark on your tests can give you false confidence. It feels like the code is covered, when really it's only been checked against the one scenario everyone already assumed would work.
How to catch it: Require tests to be generated alongside the code, not bolted on afterward as a formality. And when you're prompting for tests, ask explicitly for edge cases, empty inputs, wrong types, network failures, and unexpected user behavior. AI won't reach for the ugly cases on its own unless you tell it to.
Sometimes the problem isn't that the AI got something wrong; it's that it solved a simple problem in a needlessly complicated way. You'll see extra abstraction layers that don't serve a real purpose, config options nothing in your app actually uses, or dependencies pulled in to solve a problem you could've handled in five lines of code.
It's not broken, exactly. It's just more than it needed to be, and every extra layer is one more thing someone on your team has to understand, maintain, and eventually debug, long after the AI that wrote it is out of the conversation.
How to catch it: Make "Could this be simpler?" a standard question in every review, not an occasional afterthought. When you spot over-engineering, refactor toward the simplest solution that actually satisfies the requirement, not the most impressive-looking one.
None of this is an argument against AI-assisted development. AI is genuinely changing what's possible, and the speed it gives you is real and valuable. The issue isn't the tool it's how it gets used.
The mistake most teams make is treating AI-generated code the same way they'd treat code written by a senior engineer they've worked with for years, someone whose judgment they already trust. But AI-generated code deserves the same scrutiny you'd give a stranger's pull request because in a real sense, that's exactly what it is. It has no memory of your system's history, no instinct for your edge cases, and no accountability if something breaks in production.
The good news is that none of the five red flags above require years of experience to catch they just require you to actually look. Check your imports. Trace your logic instead of skimming it. Run a security scan. Write real tests, including the ugly ones. Ask if there's a simpler way. These are habits, not talents, and they're learnable by anyone shipping code today, regardless of how many years they've been doing it.
If you're a product owner without a technical background, this is your permission to ask harder questions before something ships you don't need to read the code yourself to ask whether it's been reviewed, tested, and checked for exactly these five things. And if you're a developer, mid-level or senior, this is simply what code review looks like now, applied to a new kind of author. AI can write your code faster than you ever could. Whether that code is actually good is still entirely up to you.
A lot of AI-generated code ships with no tests at all, or with tests that only check the happy path the one scenario where every input is exactly what was expected and nothing goes wrong. That's the easiest case to test, and also the least useful one, because real usage is rarely that clean.
The danger here is that a green checkmark on your tests can give you false confidence. It feels like the code is covered, when really it's only been checked against the one scenario everyone already assumed would work.
How to catch it: Require tests to be generated alongside the code, not bolted on afterward as a formality. And when you're prompting for tests, ask explicitly for edge cases, empty inputs, wrong types, network failures, and unexpected user behavior. AI won't reach for the ugly cases on its own unless you tell it to.
Sometimes the problem isn't that the AI got something wrong; it's that it solved a simple problem in a needlessly complicated way. You'll see extra abstraction layers that don't serve a real purpose, config options nothing in your app actually uses, or dependencies pulled in to solve a problem you could've handled in five lines of code.
It's not broken, exactly. It's just more than it needed to be, and every extra layer is one more thing someone on your team has to understand, maintain, and eventually debug, long after the AI that wrote it is out of the conversation.
How to catch it: Make "Could this be simpler?" a standard question in every review, not an occasional afterthought. When you spot over-engineering, refactor toward the simplest solution that actually satisfies the requirement, not the most impressive-looking one.
None of this is an argument against AI-assisted development. AI is genuinely changing what's possible, and the speed it gives you is real and valuable. The issue isn't the tool it's how it gets used.
The mistake most teams make is treating AI-generated code the same way they'd treat code written by a senior engineer they've worked with for years, someone whose judgment they already trust. But AI-generated code deserves the same scrutiny you'd give a stranger's pull request because in a real sense, that's exactly what it is. It has no memory of your system's history, no instinct for your edge cases, and no accountability if something breaks in production.
The good news is that none of the five red flags above require years of experience to catch they just require you to actually look. Check your imports. Trace your logic instead of skimming it. Run a security scan. Write real tests, including the ugly ones. Ask if there's a simpler way. These are habits, not talents, and they're learnable by anyone shipping code today, regardless of how many years they've been doing it.
If you're a product owner without a technical background, this is your permission to ask harder questions before something ships you don't need to read the code yourself to ask whether it's been reviewed, tested, and checked for exactly these five things. And if you're a developer, mid-level or senior, this is simply what code review looks like now, applied to a new kind of author. AI can write your code faster than you ever could. Whether that code is actually good is still entirely up to you.