Claude Code Keeps Lying: How to Handle AI Hallucinations in Your Code
Z
Zack Saadioui
8/10/2025
Claude Code Keeps Lying: How to Handle AI Hallucinations in Your Code
Ever had that weird feeling when your AI coding assistant, maybe even a smart one like Claude, gives you a piece of code that looks… PERFECT? The logic seems sound, the syntax is clean, it even has comments. You plug it in, feeling like you just saved yourself a solid hour of work.
And then you run it.
Nothing. Or worse, a cascade of errors that make absolutely no sense. You dig in, line by line, until you find the culprit: a function call that looks completely legit, something like
1
database.optimize_query_speed()
. It should exist. It feels like it exists. But it doesn't. Not in the library you're using, not anywhere. Your AI just… made it up.
If this sounds familiar, you're not alone. This phenomenon is called an "AI hallucination," & it's one of the biggest gotchas in the world of AI-assisted software development. It’s not that Claude is intentionally lying to you; it's more complicated & frankly, more interesting than that.
Here’s the thing, as someone who spends a LOT of time in this space, I’ve seen it all. The ghost functions, the phantom packages, the beautifully written but utterly broken code. So, let’s talk about what's really going on & how you can keep your projects on track without wanting to throw your laptop out the window.
What Exactly Are AI Hallucinations in Coding?
First off, let's get the terminology straight. An AI hallucination is when a model like Claude, GPT, or Copilot generates information that is plausible-sounding but factually incorrect, nonsensical, or not grounded in its training data. In plain English? The AI is confidently making stuff up.
When it comes to code, these aren't just simple typos. They are much sneakier. Some common examples I’ve seen pop up again & again are:
Inventing Functions & Methods: This is the classic example. The AI knows you want to perform an action, say, "update a user profile." It’s seen thousands of examples of code that does this. So, it generates a call to a function like
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user.updateProfile(details)
. It looks right, but the actual function in your framework might be
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user.update(details)
or
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updateUser(details)
. The AI hallucinated a function that doesn't exist.
Hallucinating API Endpoints: This one is a nightmare for anyone working with integrations. You ask for code to fetch customer data, & the AI generates a perfect-looking API call to
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/api/v2/getCustomerProfile
. But the real endpoint is
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/api/v2/customers/{id}
. The AI just extrapolated from other common endpoint names it's seen.
Making Up Package & Library Names: This is especially dangerous. An AI might suggest you import a package called
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super-fast-json-parser
. You search for it on npm or PyPI, & you either find nothing, or worse, you find a malicious package that some clever hacker created to take advantage of this exact type of hallucination. This is a real security risk.
Flawed Logic & Silent Errors: Sometimes the code runs without any immediate errors, but it does the wrong thing. The logic is subtly flawed in a way that isn't obvious. This is the scariest kind of hallucination because it might not be caught by compilers or basic tests, silently corrupting data in production.
The crazy part is how convincing it all looks. The code is often well-formatted, follows style guides, & seems to fit right into your project. The AI isn't trying to deceive you; it's just trying to complete the pattern based on the trillions of data points it was trained on. It doesn't know that a function doesn't exist; it just knows that, statistically, a function like that should exist there.
Why Does This Even Happen? It's Not Magic, It's Math
It’s easy to get frustrated & think the AI is just being lazy or, well, lying. But the root cause is fascinating. Large Language Models (LLMs) are, at their core, incredibly complex prediction engines. They work by predicting the next most likely word (or "token") in a sequence.
When you ask it to write code, it’s not thinking like a human developer. It's not reasoning about your codebase. It’s pattern-matching. It’s thinking, "Based on millions of public code repositories, what token most often comes after
1
app.use(
?"
This leads to a few key problems:
The "Always Be Helpful" Problem: Models are trained to always provide an answer. They are rarely trained to say, "I don't know." So if it doesn't have the perfect answer, it will construct the most probable-sounding one. It's filling in the gaps with its best guess.
Outdated or Incomplete Training Data: The model's knowledge is frozen at the time of its last training run. It doesn't know about the brand-new version of a library you just installed, or the specific private functions within your company's codebase.
Overgeneralization: The model sees patterns across thousands of different libraries & frameworks. It might mix up the syntax for a function in Django with a similar-looking one in Rails because the overall pattern is so similar.
Turns out, this is a HUGE issue. One 2024 study found that over 42% of code snippets from major AI coding tools contain hallucinations. Another found that developers spend a significant amount of time—for 62% of them—just fixing AI-generated errors. This isn't some rare, quirky glitch; it's a fundamental part of how these tools work right now.
Even the creators of these models are aware of it. Anthropic, the company behind Claude, actually programmed its model to explicitly warn users that it might hallucinate when asked about obscure topics, which is a pretty honest approach. They know their model isn’t perfect.
Okay, I'm Freaking Out a Little. How Do I Handle This?
So, is the solution to just give up on AI for coding? ABSOLUTELY NOT. These tools are still incredibly powerful for brainstorming, boilerplate code, learning new concepts, & getting a solid first draft. You just have to change your mindset.
Stop thinking of your AI as an autonomous programmer. Start thinking of it as an incredibly fast, slightly unreliable, but very eager junior developer. You wouldn't let a junior dev push code straight to production without a review, would you? Of course not.
Here’s your practical, no-nonsense playbook for working with AI & keeping hallucinations in check.
1. Trust, But ALWAYS Verify
This is the golden rule. Never, ever copy-paste code directly into your production branch without understanding it & testing it.
Read the Code: Read through every single line. Does it make sense? Do you recognize all the function calls & libraries?
Run It in Isolation: Before plugging it into your main application, run the generated code in a separate test file or a REPL. Give it some sample inputs & see what it outputs. Ask the AI to give you a "minimally reproducible example" so you can test its logic on its own.
Check the Docs: If you see a function or library you don't recognize, your first step should be to check the official documentation. Don't just Google it—that can lead you down a rabbit hole of similarly named but incorrect things. Go straight to the source.
2. Become a Master of Prompt Engineering
The quality of your output is directly tied to the quality of your input. Vague prompts get vague (and often hallucinated) answers.
Be Hyper-Specific: Instead of "write a function to upload a file," try "write a Python function using the
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boto3
library to upload a file to an AWS S3 bucket, with error handling for file-not-found & permission-denied exceptions."
Provide Context: Give the AI the surrounding code. Paste in the class definition, the other functions it will interact with, or the data structure it will be handling. The more context it has, the less it has to guess.
Iterate & Refine: Don't expect the perfect answer on the first try. Get a first draft, then tell the AI what to change. "That's good, but can you modify it to use async/await & add a timeout of 30 seconds?"
3. Use Your Existing Tools Religiously
Your standard development workflow is your best defense against hallucinations.
Linting & Static Analysis: A good linter will immediately flag things like undefined variables or functions, which is a dead giveaway of a hallucination.
Unit & Integration Tests: This is HUGE. Even if the code compiles, a good test suite will catch logical hallucinations where the code runs but produces the wrong output. If an AI generates code, the first thing you should do is write a test for it.
Static Application Security Testing (SAST): These tools are designed to find security vulnerabilities in your code before you run it. One study found that 40% of code generated by GPTs had vulnerabilities. A SAST tool can help catch insecure patterns that the AI might have picked up from public code.
4. The Human-in-the-Loop is You
At the end of the day, you are the final checkpoint. Rigorous code reviews are more important than ever. When reviewing code that was co-written by an AI, pay special attention to:
Dependencies: Are any new libraries being imported? If so, are they legitimate, well-maintained, & necessary?
Edge Cases: AIs are notoriously bad at thinking through edge cases. What happens if an input is null? Or a file is empty? Or a network request fails? You need to think through these scenarios.
Business Logic: The AI has no idea what your business goals are. It might write a perfectly functional piece of code that completely misunderstands the business requirement.
Where Does This Leave Us? The Rise of Specialized AI
The challenges with general-purpose models like Claude for coding highlight a really important trend: the need for specialized, reliable AI for specific business tasks. It’s one thing to have a coding assistant hallucinate a function in a side project. It's another thing ENTIRELY to have an AI that interacts with your customers making things up.
This is honestly where I see a big divide happening. For creative & development tasks, we’ll use these broad, powerful—but sometimes flaky—models as co-pilots. But for mission-critical business operations, you need something more controlled.
This is where a platform like Arsturn comes into the picture. Instead of a model trained on the entire internet, Arsturn helps businesses build custom AI chatbots trained only on their own, specific data—like their help docs, product info, & internal knowledge bases. This means when a customer asks a question, the chatbot provides instant, ACCURATE support because its entire world is the information you gave it. It can’t hallucinate about your return policy because it has the actual policy right there. It’s designed for reliability & trust, providing 24/7 engagement on your website without the risk of going off-script. It's about using a focused, no-code AI to build meaningful, personalized connections with your audience, boosting conversions without the unpredictability of a general model.
Wrapping It Up
Look, AI hallucinations in coding are a real thing, & they're not going away tomorrow. But they aren't a reason to panic. They're a reason to be smarter, more diligent, & more intentional about how we use these amazing new tools.
Treat your AI like a partner, not an oracle. Question its output. Test everything. And lean on your own skills as a developer—your critical thinking, your problem-solving abilities, & your domain knowledge. The AI can write the code, but you’re the one who has to understand it, own it, & make it great.
Hope this was helpful. I'd love to hear about the wildest AI code hallucinations you've encountered. Let me know what you think