How to Tell If Code Is AI Generated: Quick, Trusted Checks

Knowing how to tell if code is AI generated is less about playing detective and more about responsible software development. It's a practical skill that boils down to a few key approaches: a keen eye for stylistic quirks, the use of smart automated tools, and a bit of hands-on testing to see if the code actually holds up. As AI coding assistants become more common, these checks help us maintain code quality, keep our projects secure, and ensure we have clear ownership of our work.

Why We Need to Spot AI-Generated Code

AI coding assistants are quickly becoming a standard part of a developer’s workflow, blurring the line between human and machine-written code. The goal isn’t to ban these powerful tools or micromanage developers. It’s about integrating them responsibly and keeping our standards high. Knowing where a piece of code came from is essential for a few critical reasons.

The need to identify AI-generated code isn't just theoretical; it’s driven by real-world business and technical risks. For any development team, the main motivations are:

• Managing Security Risks: AI models learn from enormous datasets, including public codebases that might have old, unpatched vulnerabilities. It's surprisingly easy for AI to suggest code that inadvertently introduces these same security flaws into your project. You can read more about how generative AI has impacted security here: https://promptaa.com/blog/how-has-generative-ai-affected-security.
• Maintaining Code Quality: An AI might spit out code that works, but it often lacks the broader context of your project. This can lead to solutions that are overly complex, inefficient, or just plain hard to maintain down the line. Human oversight ensures the code actually fits the project’s architecture and long-term goals.
• Clarifying Ownership and IP: In both corporate and open-source settings, knowing who—or what—wrote the code is fundamental for intellectual property (IP) rights and license compliance. Undocumented AI-generated code can create a legal mess nobody wants to clean up.

The Rise of AI in Development

AI coding tools aren't just for early adopters anymore. By 2026, it's estimated that 84% of developers will be using or planning to use AI tools. In fact, 51% of professional developers already rely on them daily.

This massive shift means a huge chunk of code in modern applications has been at least partially written by an AI. As more companies strive to become an AI-first company, this trend will only accelerate, making detection a standard part of any solid quality assurance process.

One major study achieved a 95% true positive rate in identifying AI-generated code across more than 31 million contributions. This shows just how sophisticated detection methods are becoming.

A Quick Guide to AI Code Detection Methods

The following table provides a high-level summary of the main strategies for spotting AI-generated code. Since no single method is foolproof, the best approach is to combine these techniques for a more accurate picture. We'll explore each of these in detail throughout this guide.

Detection Method	Key Indicators	Best For
Manual Review	Inconsistent style, generic variable names, "unnatural" perfection, and overly simple comments.	Detailed peer reviews, small codebases, and training developer intuition.
Automated Tools	Statistical anomalies, complexity metrics, and patterns learned from large code datasets.	Integrating into CI/CD pipelines, scanning large volumes of code, and flagging code for deeper review.
Behavioral Testing	Poor handling of edge cases, logical flaws under stress, and unexpected failures with varied inputs.	Verifying the robustness and reliability of critical functions and algorithms.

By weaving these approaches into your workflow, you can create a strong framework for managing AI-assisted code. This allows your team to benefit from the speed of AI without ever sacrificing the quality, security, and integrity that human expertise brings to the table.

Spotting AI Code During a Manual Review

While automated tools can certainly help, nothing beats a developer's own intuition for sniffing out AI-generated code. A manual code review is where you’ll catch the subtle quirks and oddities that a machine just can’t see. Learning to spot AI code is a bit like developing an ear for music—with enough practice, you start to hear when a note is just slightly out of tune.

This isn’t about being a gatekeeper or finding flaws for the sake of it. It's about making sure every line of code we ship meets our standards for quality, readability, and long-term maintenance. Think of AI as a helpful junior dev; it can speed things up, but the senior developer who merges the pull request is ultimately responsible for the code.

The Tell-Tale Signs of Unnatural Perfection

One of the first things that often jumps out is code that looks too perfect. It's formatted flawlessly, uses textbook algorithms for even the simplest tasks, and has zero typos or weird formatting quirks.

Code written by humans, even seasoned pros, tends to have a certain "lived-in" quality. You'll find commented-out lines from old experiments, slightly unconventional (but clever) solutions, and minor imperfections. AI-generated code often feels sterile and completely detached, lacking that human fingerprint.

Another dead giveaway is redundant or overly simplistic commenting. Be on the lookout for comments that state the painfully obvious, like this:

// Function to add two numbers function add(a, b) { // Return the sum of a and b return a + b; }

A human developer would almost never write comments like that. AI models, however, are trained on countless tutorials and docs, so they have a habit of over-explaining things that don't need explaining.

Zeroing In on Naming and Variable Choices

The names chosen for variables and functions can tell you a lot. AI models often fall back on generic, placeholder-style names that give you no real context about what they're for.

Keep an eye out for a suspicious number of names like:

• data
• result
• temp
• items
• processData()
• handleInput()

Sure, we all use these from time to time. But when you see a function littered with item1, item2, and dataArray without any hint of what they actually represent (like user, product, or customerOrders), it’s a good sign the code was written without any real domain knowledge.

Reviewing code is harder than writing it. A developer needs to feel confident they can modify or extend a piece of code in the future. If the logic feels alien or the naming obscure, it undermines that confidence, regardless of who—or what—wrote it.

This lack of context is a fundamental weakness. A human thinks, "This variable holds a list of customer profiles," and names it customerProfiles. An AI might just see an array of objects and call it dataList.

Spotting Clashing Styles

Most files in a mature codebase have a consistent style, a rhythm. When a block of AI-generated code gets dropped in, it can stick out like a sore thumb.

Maybe your team has standardized on for...of loops, but a new function suddenly uses an old-school C-style for (let i = 0; ...) loop. Or perhaps the entire file uses arrow functions, but one new section uses the function keyword for every declaration. These stylistic clashes are often a sign that the code came from somewhere else—an external source that had no idea about your project’s established patterns.

The truth is, trust in AI-generated code is shaky at best. By 2026, only 29% of developers reported trusting its accuracy, an 11-point drop from the previous year. Research from CodeRabbit shows that AI-assisted code generation produces 1.7 times more issues related to logic and correctness bugs than traditional methods. What's more, about 66% of developers say AI frequently produces code that is 'almost correct' but still wrong, making the review and fix cycle even harder. You can discover more insights about these AI code quality findings and what they mean for your team.

Noticing Inappropriate Complexity or Boilerplate

Sometimes the code isn't necessarily bad, but it’s just… weirdly complex for the task at hand. An AI might spit out a hyper-optimized sorting algorithm for a list that you know will never have more than ten items. This is a classic symptom of a general-purpose solution being applied without understanding the specific, limited context of the problem.

On the other end of the spectrum, you might find a ton of unnecessary boilerplate. For instance, an AI might generate a full-fledged error-handling class for a simple script that just needed a basic try...catch block. This happens because the model is pulling from a vast library of "best practices" without asking if they’re actually appropriate for the scale of the task.

When the solution feels way bigger than the problem, it’s a good time to ask where it came from.

Using Automated AI Code Detection Tools

Manual reviews are fantastic for spotting those subtle, hard-to-catch flaws, but let's be realistic—they just don't scale. When you're managing a large codebase or a flood of pull requests, you need a faster way to get an initial read on where the code came from. That’s where automated tools come in, acting as your first line of defense.

These tools are far more than simple pattern matchers. By 2026, the best AI code detectors have become remarkably sophisticated. They analyze a whole range of statistical properties—things like code complexity, function length, and even the "entropy" or randomness of the code's tokens, which often looks quite different when a machine writes it versus a human.

They learn these tell-tale signs by training on massive datasets, filled with millions of code samples from both human developers and a wide variety of AI models. This process builds a deep statistical profile of what "human code" looks like compared to "AI code."

How These Detectors Work

When you feed a code snippet into an automated detector, it spits out a confidence score. You might see something like "87% likely to be AI-generated." It's critical to remember this is a probability, not a definitive verdict.

A high score doesn't mean "this is bad code." It's more of a flag that says, "Hey, this code has a lot of the hallmarks of AI generation, and it probably warrants a closer look."

The image above breaks down some of the key heuristics that both manual reviewers and automated tools look for, such as mismatched styles, odd variable naming, and an unnatural level of perfection.

Comparison of AI Code Detector Features

Choosing the right tool depends entirely on your team's workflow and needs. Some tools are standalone, while others plug right into your existing CI/CD pipeline. Here's a quick comparison to help you navigate the options.

Tool Name	Detection Method	Primary Use Case	Integration Support
GPTZero	NLP model analysis, perplexity & burstiness scoring	Educational settings, content integrity, initial code scans	API, LMS integrations (e.g., Canvas, Blackboard)
Sapling	Transformer-based AI detection	Business writing, customer support, basic code analysis	Browser extensions, API, Google Docs/Outlook add-ins
Codeleaks	Proprietary AI detection algorithms	Plagiarism checking, code provenance, IP protection	API, CI/CD plugins, IDE extensions
Originality.ai	Multi-model analysis for text and code	SEO content, academic integrity, advanced code reviews	API, Web app

This table shows that while some tools focus on educational integrity, others are built with developers in mind, offering direct integrations into the pipelines where you work every day.

Integrating Detectors into Your Workflow

The real magic happens when you weave these tools directly into your development cycle. Instead of manually spot-checking files, you can automate the entire process.

Here are a few practical ways to do it:

• Pre-Commit Hooks: You can set up a hook that automatically runs a detector on any code before a developer can even commit it. If the AI score is too high, the commit is blocked, forcing the developer to take a second look and either justify or refactor the code.
• CI/CD Pipelines: This is one of the most popular methods. When a developer opens a pull request, the pipeline triggers a scan. The results can be posted as a comment right on the PR, giving reviewers a heads-up about potentially AI-generated sections.
• IDE Extensions: Some detectors offer extensions for editors like VS Code. These provide real-time feedback as you write or paste code, offering instant insights without breaking your flow.

When you first set up these integrations, my advice is to start with a forgiving threshold. You want to avoid creating friction with a bunch of false positives. The goal is to flag the most obvious cases and use the tool to start a conversation, not to be a gatekeeper. For teams looking to build robust and observable AI systems, you can learn more about creating traceable workflows in our guide to LangSmith.

A good rule of thumb: Treat the detector's output like another linter. It’s a signal that something might be off, prompting a human to take a closer look. Never configure it to automatically reject code without a person in the loop.

Ultimately, automated tools give you a fast, efficient way to triage huge amounts of code. By using their scores as a guide for deeper, manual inspection, you can focus your team's valuable review time where it's needed most. This blend of automated signaling and human judgment is the key to responsibly managing AI-assisted development at scale.

How to Spot AI by Testing How the Code Behaves

Looking at code on a screen only gets you so far. The real test is seeing what it does when it runs. To truly figure out if you're looking at AI-generated code, you have to push it, prod it, and see how it reacts under pressure.

This is where you can often expose a key weakness of AI code: brittleness. An AI might write a function that works perfectly for the textbook example it was trained on. But throw a slightly weird or unexpected input at it, and the whole thing can fall apart in ways a human developer would have instinctively guarded against.

Your job is to find those breaking points. Think less about confirming what works and more about discovering what doesn't.

Probing for Edge Cases and Logical Gaps

I always start by throwing curveballs at the code—things AI models often miss because they haven't experienced the chaos of real-world user input. They're great at the "happy path" but tend to stumble when the path gets bumpy.

Here are some of my go-to tests for sniffing out brittle AI code:

• Null and Undefined Inputs: What happens when a function gets null instead of the object it expects? AI-generated code often forgets these basic checks, leading to ugly TypeError exceptions that can crash an entire process.
• Empty Data: Try feeding it empty strings, empty arrays, or objects with no keys. Does the code handle this gracefully, or does it assume it'll always have data to work with?
• Extreme Values: Push the boundaries with huge numbers, negative numbers, or just plain old zero. Testing a calculation with 0 is a classic way to find division-by-zero errors the AI never considered.
• Wrong Data Types: Give it a number when it wants a string, or an object where it expects an array. Good code written by a human will typically handle this gracefully. Brittle AI code often won't.

When a function implodes because you handed it an empty array, it’s a massive red flag. It tells you the code was probably written without any real thought about how it would be used in the wild.

Using a Test-First Mindset to Catch the AI

You can take this a step further by flipping the script with a test-driven development (TDD) approach. Instead of writing tests for the code to pass, you write tests you suspect the AI-generated code will fail.

Imagine you ask an AI to write a Markdown-to-HTML converter. A great prompt would be: "First, write unit tests for a function that converts Markdown to HTML. Include tests for headers, bold text, and lists. Then, write the code that makes those tests pass."

Forcing the AI to write its own tests is incredibly revealing. You get a direct look at its "understanding" of the problem. If its tests are naive and miss obvious edge cases, you can bet the implementation will be just as shaky.

Watching this process unfold gives you concrete evidence. The AI might write tests, generate code, run them, and then get stuck when they fail. Or, it might try to "fix" the code with a nonsensical change. Either way, you've exposed its limits.

This is also a fantastic way to make the code better. You can grab an error from your production logs, feed it to the AI, and say, "Write a new test that reproduces this specific error. Then, fix the code so all tests pass." Suddenly, a bug report becomes a permanent improvement and a stronger test suite.

Ultimately, running the code provides the hard proof that a manual review can only guess at. You move from saying, "This looks a little off," to, "This is demonstrably broken under these conditions." Integrating these behavioral checks is your best defense against the subtle but serious flaws that often hide in AI-generated code.

Investigating Code Provenance and History

Sometimes, the most telling clues aren't in the code itself, but in the story of how it got there. To figure out if a chunk of code was written by an AI, you need to put on your detective hat. The code's origin story—its provenance—is often hiding in plain sight within your version control system.

Your first stop should be the Git history. A commit log is a narrative, and contributions from AI tools often tell a strange tale. Watch out for unusual patterns in how often someone commits and how large those commits are.

Think about this scenario: a brand-new contributor suddenly drops a pull request with thousands of lines of complex code. And it has zero comments. That’s a massive red flag. Real developers usually start with small, incremental changes as they get familiar with a project. A huge, flawless first commit often means the code came from somewhere else—like an AI.

Analyzing Commit Patterns and Messages

The timing and style of commits can also give the game away. Are you seeing a bunch of commits pushed in an impossibly short amount of time? If a developer ships three complicated features in under 10 minutes, they probably didn't type all that code themselves. It’s far more likely it was generated and committed in rapid succession.

Commit messages are another treasure trove of evidence. Vague, generic messages are a classic tell.

• "fix bug"
• "add feature"
• "update file"

Developers who work on teams tend to write descriptive messages explaining why they made a change, not just what they did. AI-generated messages often miss this crucial context. A commit history littered with these kinds of unhelpful notes should make you look closer.

When you're reviewing a contribution, the story the commits tell should make sense. A history of small, logical, well-documented changes builds trust. A chaotic or vague history is a sign you need to ask more questions.

Understanding the Developer's Context

Don't forget to consider the developer's background. Is this person known for using AI coding assistants? Was the code written during a pair programming session where AI tools were in the mix? This isn’t about pointing fingers; it’s about gathering all the facts.

To get ahead of this, many teams are now setting clear policies for AI use. This is a smart move. Simply asking developers to tag AI-assisted commits, for instance, eliminates the guesswork and fosters an honest, transparent culture. It creates a clean, auditable trail right in your history.

For a deeper dive into making your codebase more trustworthy, especially when AI is involved, learning more about how to build audit-proof AI systems is essential.

By mixing Git analysis with clear internal guidelines, you can turn suspicion into certainty. This ensures every piece of code—whether written by a human or an AI—is fully understood before it gets merged. This kind of digital detective work is becoming a critical skill for modern software teams.

Building a Healthy Human-AI Development Team

So far, we’ve been deep in the weeds of detection—spotting AI-generated code through manual reviews, automated tools, and running tests. But if we only focus on catching AI in the act, we risk creating a culture of suspicion. The real goal isn't to play "gotcha" with developers. It's to ship better software, faster, as one team.

This means we need a different mindset. Instead of treating AI coding assistants like they're forbidden, let's treat them like what they are: powerful tools that require skill and transparency to use well. The most successful teams I've seen aren't banning AI; they're setting clear, fair ground rules that encourage people to use it responsibly.

Fostering Transparency Over Suspicion

A great team runs on openness, not fear. When developers feel like they have to hide their AI use, they’re far less likely to ask for help or admit they don't fully understand a chunk of generated code. That’s a breeding ground for subtle, nasty bugs.

The first step is to just start talking about it. Let's be honest: a huge number of developers—some senior engineers estimate around 80%—are already using AI. By openly discussing where it shines and where it stumbles, you pull it out of the shadows. It stops being a secret weapon and becomes just another tool in the modern dev toolkit, no different than an IDE or a linter.

Establishing Clear and Fair AI Usage Policies

Without clear guidelines, developers are forced to guess what's okay and what's not. That ambiguity creates inconsistency and, eventually, conflict. A solid AI usage policy shouldn't be handed down from on high; it should be a living document that the whole team helps build.

A good policy gives direct answers to the tough questions:

• When is it appropriate to use AI? Is it great for boilerplate and unit tests but off-limits for core business logic?
• How should we disclose its use? Should developers flag AI-assisted commits or just mention it in a pull request?
• Who is ultimately responsible? Make it crystal clear that the developer who commits the code is 100% accountable for its quality, security, and performance, no matter where it came from.
• Are any tools off-limits? Some AI tools might not align with your company's data privacy or IP rules. Specify which tools are approved and which are not.

The policy isn’t about restricting developers. It’s about giving them the confidence to use powerful tools responsibly, with the full backing of their team and leadership. It protects both the developer and the company.

When you create these rules together, everyone understands the "why" behind them and feels a sense of ownership. That's always more effective than a top-down mandate. For a closer look at how AI fits into development workflows, check out our guide on using an LLM for code. It provides some great context for shaping these policies.

Shifting from Policing to Mentorship

Once you have a policy in place, the role of a team lead or senior dev changes. You're no longer a cop; you're a coach. When you spot a piece of code that looks AI-generated and isn't quite up to par, it becomes a teaching moment. Instead of asking, "Did an AI write this?", you can steer the conversation in a more constructive direction.

Try framing your feedback around the team's shared goals:

• "This function works, but I’m worried about how it will perform under heavy load. Let's walk through it and see if we can simplify it."
• "I noticed this section has a different style from our other code. Let's refactor it to match our standards so it's easier for everyone to maintain."
• "This is a creative solution, but it feels a bit complex. Can you walk me through the logic? I want to make sure I could debug it down the road."

This simple change reframes the entire code review process around collaboration and quality. It reinforces the critical idea that human oversight is what turns AI's raw output into robust, production-ready code. By focusing on shared standards and mentorship, you build an environment where developers can master these new tools, blending human expertise with machine efficiency to create truly great software.

FAQs: Navigating AI Code in Your Workflow

As more dev teams start using AI coding assistants, the same questions keep coming up. Spotting AI-generated code is one thing, but knowing how to manage it professionally is a whole different ballgame. Let's tackle some of the most common concerns.

Can AI Code Detectors Ever Be 100% Accurate?

In a word, no. No tool is foolproof. It's best to think of AI code detectors as a first line of defense—a signal that a piece of code needs a closer look from a human. They're great at spotting statistical patterns, but modern AI can produce code that’s nearly impossible to distinguish from something a person would write.

Treat these tools as a way to guide your review process, not replace it. They can point you to potential trouble spots, but they should never be the final judge.

A high score from a detector doesn't mean the code is automatically AI-generated or bad. It's just a flag. It means that particular snippet has characteristics that warrant a second look from an experienced developer. The final call is always yours.

Should We Just Ban AI Coding Assistants Altogether?

An outright ban on AI tools is usually a bad idea. It's not practical, and it can put your team at a serious disadvantage. These assistants are becoming a standard part of the developer's toolkit, helping to speed up tedious tasks and churn out boilerplate code.

A much better approach is to create clear, sensible guidelines.

Your policy should spell out:

• When and how your team should use these tools.
• If and when developers need to disclose that they used an AI assistant.
• That all code, no matter where it came from, must go through rigorous human review and testing.

This way, you get the productivity boost from AI without sacrificing your standards for quality and security.

What's the Single Biggest Giveaway of AI-Generated Code?

If I had to pick just one thing, it would be a lack of context. This is when a block of code just feels… off. It doesn't quite fit with everything else around it.

Maybe you see a ridiculously complex, hyper-optimized algorithm dropped into a simple, straightforward part of the application. Or you'll notice a chunk of code written in a style that's completely alien to the rest of the file.

Another classic sign is a massive, uncommented commit from a junior developer that’s way outside their usual pattern. AI models don't have the big-picture understanding of a project that a human does, and that "context blindness" is often the dead giveaway.

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