Why AI-Generated Solutions Can Lead to Complex Debugging Issues

AI tools like ChatGPT and Copilot can write beautiful, clean JavaScript — but that doesn’t mean it’s safe.

These days, it’s tempting to use AI to refactor or write our code. Ask something like ChatGPT to simplify your function, and boom — you get a chained, elegant one-liner using .map(), .filter(), .reduce() and more.

It looks clean.
It feels modern.
But it can be a debugging nightmare.


🚨 The Illusion of Clean Code

Here’s an example AI-generated function I used:

const result = data
  .filter(item => item.active)
  .map(item => item.name.trim())
  .sort()
  .slice(0, 5)
  .join(', ');

Looks perfect, right?

Then someone reported:

"Names are missing. Also, the app crashes sometimes."

Hmm. I try to debug — but where do I even console.log()?

  • Is the issue in .filter()?

  • Or is item.name undefined?

  • Or does .trim() throw on null?

Eventually, I realized the problem: item.name was null.
So .trim() failed and crashed the whole chain.

🧠 Refactor to Breathe

So I rewrote it — not to be clever, but to be clear:

const activeItems = data.filter(item => item.active);
console.log("Active items:", activeItems);

const trimmedNames = activeItems.map(item => {
  if (!item.name || typeof item.name !== 'string') {
    console.warn("Invalid item:", item);
    return null;
  }
  return item.name.trim();
});

const validNames = trimmedNames.filter(Boolean).sort();
const topFive = validNames.slice(0, 5);
const result = topFive.join(', ');
console.log("Final result:", result);

It’s:

  • ✅ Readable

  • ✅ Traceable

  • ✅ Safer

Sometimes you don’t need a one-liner — you need clarity.

💡 What I Learned

  • AI-generated chaining ≠ safe chaining

  • Chaining works great — if the data is clean

  • But in most real-world apps, your data is a little messy

  • Debugging tightly chained methods? Like untangling Christmas lights… blindfolded


🔧 My Rule of Thumb

If I can't easily log or debug each step, I shouldn't compress it.

✅ Use chaining when:

  • You trust your data

  • Each method is short and clear

  • You're not worried about side effects

❌ Break the chain when:

  • You need to inspect values in-between

  • The logic is non-trivial

  • You’re collaborating with others (or future-you)

🧪 Bonus: A Real-World Case

AI once gave me this:

const result = orders
  .filter(o => o.items.length > 0)
  .map(o => o.items.map(i => i.price).reduce((a, b) => a + b))
  .filter(total => total > 100);

It looked brilliant. Until it didn’t.

What if:

  • items is empty?

  • price is missing?

  • reduce() hits undefined?

So I rewrote that too — step by step.

Not because I didn’t know how to chain —
But because I care more about debugging and safety than flexing.


🔚 Final Thoughts

Method chaining is powerful — but AI often overdoes it.

If your code feels like a magic trick, it’s probably a trap.

Break the chain when you need to.
Your future self — and your teammates — will thank you.


✍️ Thanks for reading!
Have you seen AI write a beautifully broken one-liner? Drop your story below 👇

Originally published on Medium

1
Subscribe to my newsletter

Read articles from Rahul N Jayaraman directly inside your inbox. Subscribe to the newsletter, and don't miss out.

Written by

Rahul N Jayaraman
Rahul N Jayaraman

👋 Hey, I'm Rahul — a full-stack developer who loves turning ideas into clean, functional products. I write about JavaScript, Node.js, React, and real-world dev lessons. Expect dev logs, bugs I broke (and fixed), and things I'm learning along the way. 🛠 Currently building, shipping, and writing one commit at a time.