Artificial Intelligence (AI) has transformed software development in recent years, and one of its most exciting applications is AI-assisted code generation. Tools like GitHub Copilot, ChatGPT, and Amazon CodeWhisperer promise accelerated coding, fewer bugs, and less boilerplate for developers. Yet, as more developers integrate AI-generated code into their projects, surprising pitfalls are surfacing — particularly around fake dependencies and outdated syntax.
TL;DR
AI-assisted code generation tools can boost developer productivity, but they sometimes hallucinate fake libraries or use outdated patterns. Since these tools rely on vast, imperfect training data collected from the web, developers must critically assess every suggestion. This includes checking whether a suggested dependency exists, is secure, and is actively maintained. Always vet and test AI-generated code before merging it into production codebases.
Why AI Gets It Wrong Sometimes
At the core of most AI-assisted coding tools lies a large language model (LLM) trained on publicly available code, documentation, Stack Overflow discussions, and GitHub repositories. Because not all of this training data is up-to-date, accurate, or even functional, the AI can lack the context needed to distinguish between good practices and obsolete or fictional code.
For example, suppose a model comes across a snippet using a non-existent Python package like fast-db-reader posted in an obscure blog post — the AI might assume it’s a real, stable library and suggest it in similar programming contexts.
Here are some common reasons why AI-generated code might be problematic:
- Hallucinated dependencies: The AI suggests using a library or package that doesn’t exist.
- Outdated syntax: The AI recommends syntax or APIs that have been deprecated or removed.
- Mismatched versions: The suggestions may work only in old or specific versions of a language or framework.
- Fabricated classes or functions: The AI can make up methods that seem logical but don’t actually exist in any known library.
The Problem With Fake Dependencies
One of the most misleading errors developers encounter is the inclusion of a dependency that doesn’t actually exist. The AI might suggest the following code with complete confidence:
from dataoptimizer import ProfileAnalyser
analyser = ProfileAnalyser()
analyser.load('user_data.csv')
This code looks syntactically sound and even professional. However, a quick pip install dataoptimizer would reveal that no such package exists. Even worse, a malicious actor might register that package name later — a real risk in today’s era of dependency confusion.
How to Identify Fake Dependencies
When dealing with AI-generated suggestions involving new libraries, ask yourself the following:
- Does the package exist in the official repository (e.g., PyPI, npm, Maven)?
- Is it documented in official docs or GitHub READMEs?
- How often is it updated? Is there community support?
- Are there security concerns or open issues?
Running a quick Google search or checking GitHub for the package’s existence can save hours of troubleshooting later on.
Why Outdated Syntax Keeps Sneaking In
AI models are trained on snapshots of the internet. If a model hasn’t been updated recently or was trained on codebases with older standards (say Python 2.7 or early React), it may regurgitate syntax and functions that are no longer recommended or supported.
Consider this example in Python:
print "Hello, world!" # Python 2.x syntax
If a newer developer blindly copies this code into a Python 3 environment, they’ll be greeted with a syntax error. Similarly, AI might suggest old Redux patterns or deprecated lifecycle methods in React that no longer fit the current best practices.
Common Examples of Outdated Syntax
- Python: Using
printwithout parentheses,unicodevsstr, or old-style exception handling. - React: Recommending class components and lifecycle methods instead of hooks.
- Java: Missing out on modern constructs like
var, streams, or lambda expressions. - HTML/CSS: Using vendor prefixes for properties that have been standard for years.
While these errors might not completely break your project, they usually indicate that the model skipped over newer, more efficient paradigms.
How AI “Thinks” About Code
To understand why these mistakes happen, it helps to realize that an LLM doesn’t “understand” programming in the traditional sense. It predicts the most statistically probable next token—essentially guessing what code might come next based on pattern matching. It doesn’t know whether dataoptimizer is fake or real — it just knows it’s probable given the context.
Moreover, AI systems don’t have persistent memory or access to live documentation (unless specifically integrated). So unless the model is connected to tools like Stack Overflow search plugins or real-time API docs, it cannot verify its suggestions in real time.
Tips to Vet AI-Generated Code Before Committing
Rather than rejecting AI-assisted coding altogether, developers should learn to use it responsibly. AI can be a fantastic autocomplete assistant — but one that needs supervision.
Here’s how to stay safe and ensure quality:
- Check the dependencies: Always verify that any library recommended by the AI is real, trustworthy, and appropriate for your project.
- Consult documentation: Compare the AI-generated code with official API documents or package READMEs to make sure you’re using it as intended.
- Run linters and type checkers: Tools like ESLint, Pylint, or mypy can catch syntax errors and flag outdated patterns.
- Write tests: Ensure AI-generated code has test coverage and behaves as expected under edge cases.
- Pair with human review: Use code reviews and pair programming to catch inconsistencies or potential issues the AI might produce.
AI Code is Only as Smart as Your Workflow
Think of AI-assisted coding not as replacing developers but as empowering them. It’s there to reduce repetitive typing, offer quick scaffolding, and present coding patterns from its training set. But these tools still leave human developers responsible for:
- Critical thinking
- System design
- Architecture decisions
- Security audits
Just as you wouldn’t blindly copy and paste code from a random Stack Overflow thread, you shouldn’t blindly trust code from an AI assistant.
Final Thoughts
AI code generation tools are powerful productivity enhancers, but they come with caveats. The occasional appearance of fake dependencies or outdated syntax doesn’t mean these tools are untrustworthy — rather, it emphasizes the need for careful supervision and validation.
By adding a few checks and balances into your workflow, you can enjoy the full benefit of AI-assisted coding while avoiding hours of debugging or poor design decisions down the line.
AI can write code, but only humans can write it responsibly.
