Brief History
AI is a Tool
AI-assisted coding began with tools like IntelliSense, which provided code suggestions and autocompletions. AI coding assistants are an advanced evolution of this technology, converting human language into code. However, we must remember that AI assistants are just tools, not autonomous entities capable of reasoning like humans.
Programming has evolved from binary code to high-level languages like C++ and Python, improving accessibility. Yet, even in structured languages, humans frequently introduce errors like buffer overflows, Race Conditions and logic bugs.
Natural language code generation increases accessibility—but also risks, as AI interprets human language, which is inherently vague.
Not smarter than it's creator
AI recognizes patterns, not meaning (true understanding). To avoid errors, be as specific as possible when prompting
This is why we must stay vigilant and be as specific as possible when generating code or writing prompt. Let’s look at some real-world examples where AI-generated code led to unintended outcome.
Examples of AI-Generated Code Mistakes
1) AI-generated regex
- You prompt to generate a regex pattern to validate emails.
- It produces a pattern that seems correct but rejects valid emails (e.g., john.doe@email.travel) or allows invalid ones (user@com).
Lesson
AI can generate code, but validation and testing are your responsibility.
2) AI needs context
- You prompt: "Write a function to find prime numbers in Python."
- It generates a function that works but is inefficient (O(n) instead of O(√n)).
Lesson
AI often generates functional but non-optimized solutions unless specifically prompted for efficiency.