5 Prompt Mistakes That Make AI Generate Worse Code (With Fixes)

After hundreds of AI-assisted coding sessions, I've noticed the same five mistakes killing output quality. Each one is easy to fix — once you see it. 1. Dumping the Entire File as Context The mistake: Pasting 500 lines of code and saying "fix the bug." Why it fails: The model spreads attention across irrelevant code. It might "fix" something unrelated or miss the actual issue buried in line 347. The fix: Extract only the relevant function + its dependencies. Add a one-line description of what it should do vs. what it does. Here's the `calculateDiscount` function and the `PricingRule` type it depends on. Expected: returns 0 for expired coupons. Actual: returns the full discount amount. Fix only this function. 2. Skipping the "Don't" Constraints The mistake: Telling the AI what to build but not what to avoid. Why it fails: Models are eager to please. Without boundaries, they'll add features, refactor adjacent code, or switch to a "better" library. The fix: Add explicit constraints: - Do