AI & Tools
AI Coding Assistants: What They Actually Do Well (and Poorly)
AI coding assistants are genuinely useful, but understanding their real strengths and failure modes matters more than which specific tool you pick.
What They're Genuinely Good At
- Boilerplate code -- getters/setters, standard CRUD scaffolding, repetitive test structure
- Explaining unfamiliar code or an error message in plain language
- Suggesting a reasonable first draft for a well-understood, common pattern
- Translating logic between languages you're already comfortable in
Where They Still Struggle
- Deep, project-specific business logic that depends on context the assistant doesn't have
- Confidently generating code that looks correct but has a subtle bug -- especially in edge cases
- Architectural decisions that require weighing real tradeoffs specific to your system, not a generic best practice
- Security-sensitive code, where a plausible-looking suggestion can carry a real vulnerability
Example: Catching a Subtle Generated Bug
An assistant asked to "write a function to check if a user is old enough to register (18+)" might generate age >= 18 based on a birthdate calculated with a naive year-subtraction, which is wrong for someone whose birthday hasn't occurred yet this year -- a classic off-by-one-year bug. A developer who reviews the generated code (rather than accepting it blindly) catches this in seconds; one who doesn't ships a real bug that could pass casual testing but fail for real users near their birthday.
Frequently Asked Questions
It's a genuine risk if used passively (accepting suggestions without understanding them) -- the skill of actually reasoning through a problem can atrophy without deliberate practice. Using an assistant as a first draft to critique and improve, rather than a final answer to accept, avoids this while still saving real time.
Yes -- they can generate code with real security issues (SQL injection from unescaped input, weak cryptographic choices) if not reviewed carefully, since they're trained on a mix of good and bad real-world code. Treat AI-generated code with the same security scrutiny you'd apply to any other new code, not less.