Why You Shouldn’t Use AI Coding Assistants Blindly: A Real-World Example

AI coding assistants, like ChatGPT, have become powerful tools for developers, but they are not infallible. As they evolve, it’s essential to remember that AI-generated code still requires human oversight. Trusting these assistants blindly can lead to errors, as these tools may misinterpret code or suggest unnecessary changes.

Let me show you an example where I asked ChatGPT to review a simple binary search function in Scala. The results, while well-intentioned, demonstrated why we must carefully review what AI suggests.

Here’s a basic binary search implementation I wrote in Scala:

def binarySearch(nums: List[Int], value: Int) : Option[Int] = {
  def binarySearchInt(start: Int, end:Int): Option[Int] = {
    if (start > end) None else {
      val mid = start + ((end - start) / 2)
      mid match {
        case _ if nums(mid) < value => binarySearchInt(mid + 1, end)
        case _ if nums(mid) > value => binarySearchInt(start, mid - 1)
        case _ => Some(mid)
      }
    }
  }
  binarySearchInt(0,nums.length-1)
}

I asked ChatGPT to review this code, and here’s the response I received:

Then it gave me a revised solution

Notice how it just removed the paren and gave me same code

val mid = start + ((end - start) / 2)

changed to

 val mid = start + (end - start) / 2  // This avoids potential overflow

The assistant claimed that the latter version would avoid overflow, but this explanation didn’t make much sense. Both versions are essentially the same, and neither would cause overflow in this context. While the suggestion didn’t break anything, it showed that the AI didn’t fully understand the issue.

So I asked it

This didnt make sense, so narrowed down my question

I am glad that it admitted.

The Real Issue:

What’s interesting here is that the AI’s intention was correct — it tried to improve the code by avoiding overflow — but the suggestion was unnecessary. This example highlights a critical concern: AI assistants may misunderstand the code or context, even though they are built to help.

In this case, I quickly recognized the problem and realized the advice didn’t apply. But what about situations where developers might not double-check the suggestions? It’s easy to see how blindly following AI’s recommendations could lead to bugs or unnecessary changes in a codebase.

Conclusion

AI coding assistants are useful tools, but they are not perfect. As developers, we must always remain vigilant when using them, ensuring that we review and understand the code they generate or suggest. While these tools will continue to improve, they still require a human touch for critical thinking, context, and judgment.

Always remember: AI coding assistants are here to assist, not to replace careful code review and decision-making.