การหลีกเลี่ยงข้อผิดพลาดสำคัญของโค้ดปัญญาประดิษฐ์

AI coding tools are confident, fast, and sometimes flat-out wrong. The danger isn't that they fail loudly, it's that they fail quietly, producing code that looks right and runs without errors but does the wrong thing.

In this lesson you'll learn the three most common pitfalls: hallucinations, bloat, and silent bugs, plus how to catch each one before it hurts you.

A hallucination is when the AI invents something that doesn't exist: a function, a library, an API endpoint, or a config option it just made up because it sounds plausible.

You'll see it import a package that isn't real, or call a method like array.sortDescending() that JavaScript doesn't have. The code looks reasonable until you run it and get "is not a function."

The fastest hallucination check is simply to run the code. Here the AI "helpfully" used a method that doesn't exist. Run this and read the error, that error message is your friend.

Three habits stop most hallucinations cold:

• Run early, run often. Don't stack 200 lines before testing.
• Ask for real, popular tools. Tell the AI to use well-known libraries, not obscure ones it might invent.
• Verify imports. If it imports a package, check it actually exists on npm before trusting it.

Bloat is when the AI gives you far more than you asked for: extra abstractions, unnecessary libraries, ten config files for a one-page app, or a 100-line solution to a 10-line problem.

Bloat feels productive, more code! but it's a trap. Every extra line is something you have to understand, maintain, and debug later. Lean code is a feature, not a limitation.

You can steer the AI away from bloat just by saying so. Be explicit that simplicity is the goal, AI will happily over-engineer if you don't push back.

Compare these two solutions to the same problem: get unique values from a list. Both work, run it, but the bloated one drags in extra machinery for no benefit. When AI hands you the heavy version, ask for the simple one.

The scariest pitfall: code that runs without errors but is subtly wrong. The AI handles the happy path and quietly ignores the edge cases.

Classic examples: an empty list, a missing value, a negative number, a date at midnight, a user with no name. The demo works in the meeting and breaks for a real user on Tuesday.

This "average" function looks fine and works for normal input. But run it and watch what happens with an empty list, it returns NaN instead of failing loudly. A silent bug waiting to corrupt a report.

The cure for silent bugs is to actively go looking for them. After the AI writes a function, ask it to attack its own work:

Three pitfalls, three reflexes:

• Hallucinations → run early, verify imports, ask for real tools.
• Bloat → demand the simplest version, question every extra.
• Silent bugs → make the AI test its own edge cases.

None of these require deep CS knowledge. They just require the habit of not trusting code until you've seen it behave.

An AI writes a function that runs with no errors and works in your demo, but returns a wrong number when given an empty list. What kind of pitfall is this?

You can now name and catch the big three AI failure modes:

• Hallucinations: invented functions, libraries, or APIs, caught by running code and verifying imports.
• Bloat: over-engineered solutions, cured by demanding the simplest version.
• Silent bugs: correct-looking code that fails on edge cases, hunted by making the AI test its own work.

Catching these is what separates a builder who ships reliable apps from one who ships surprises. Next: how to use AI to actually grow your own skills.