The Setup
You ask AI to build an e-commerce backend. Five minutes later, 300 lines of beautiful code. You push to production. Happy dev.
3 AM. Orders crash.
Turns out the AI wrote asynchronous database transactions — and one order got charged twice.
This isn't a joke. This happened. And worse things happen every day on projects around the world that blindly trust AI-generated code.
1. Hallucinated Dependencies
The trap: AI references a Python package that looks totally legit. Nice name, proper usage, great docs. You go pip install — doesn't exist. The AI invented it.
Why: Language models optimize for "looks correct" not "is correct." The package name sounded real in the training data, so the model generated it.
Fix: Before using any AI-recommended library, search PyPI/GitHub first. If it doesn't have real stars, real issues, and real commits — don't install it.
2. The Context Window Is Not a Brain
The trap: You dump your entire 200K-token codebase into the context, expecting omniscience. Instead, AI forgets what happened 20K tokens ago — right where your critical auth logic lives.
Why: Long context doesn't equal deep understanding. The AI processes your input as a flat sequence, not as a connected architecture.
Fix: Break big systems into small modules. Ask one question per function. Never let AI design the entire system architecture in one shot.
3. Security Blind Spots
The trap: AI-generated SQL queries — 9 out of 10 use raw string concatenation. Fast? Yes. SQL injection? Also yes.
Why: AI doesn't model adversarial thinking. It writes the happy path and calls it done.
Fix: Treat AI output as a first draft. Every SQL query, every API call, every input handler — review it like a security auditor, not a feature developer.
4. Testing That Tests Nothing
The trap: AI writes unit tests. 90% coverage. You feel great. But those tests only cover the logic you showed the AI. Edge cases? Nope. Concurrent access? Skipped. Error chains? "Too complex."
Why: AI tests what's in the prompt, not what's missing from it.
Fix: After AI generates tests, manually add boundary cases. Empty inputs. Network failures. Race conditions. That last 10% is where everything breaks.
5. Over-Engineering by Default
The trap: A colleague's project needed a simple DTO with 3 fields. AI generated a Factory pattern + Builder pattern + Strategy pattern — 5 interfaces, 3 abstraction layers. For a data transfer object with 3 fields.
Why: AI knows design patterns are "best practices." It doesn't know when they're unnecessary.
Fix: After AI generates, ask: "Can I delete half of this?" If yes, delete it.
6. The Maintenance Nightmare
The hidden cost: AI-generated code doesn't match your team's style. Six months later, nobody knows how that weird abstraction works — because nobody on the team wrote it.
Why: Each AI session starts fresh. It doesn't learn your team's patterns unless you explicitly teach it.
Fix: Never check in AI code without adding comments explaining why it made those choices. Even better — have the AI rewrite its output to match your team's style guide.
The Do/Don't Cheat Sheet
| ✅ DO | ❌ DON'T |
|---|---|
| Write single-purpose functions | Design system architecture |
| Generate test cases | Decide boundary conditions |
| Translate/refactor code | Handle production or sensitive data |
| Read every line AI writes | Copy-paste straight to prod |
| Ask AI to explain its reasoning | Accept code without comments |
| Tell AI your exact tech stack | Let AI guess your config |
The Action Plan
If you're using AI for coding (or planning to), spend 10 minutes a day on three things:
- Keep code review. Every AI output — review it like a hacker would. "How would I break this?"
- Start a "Fail Log." Every time AI code breaks in production, log it. Three months later, your failure rate will drop to near zero.
- Pair-program with AI. Write the core logic yourself first, then ask AI for alternatives. Compare. Pick the winner.
What's your worst AI coding fail? Drop it in the comments — I promise I'll only smirk a little. 🐎
Inspired by recent Dev.to discussions and my own 6-month AI coding log.
*Follow for daily AI field notes — no fluff, just what works.
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