When to Trust AI (and When Not To)
AI coding assistants are remarkably capable, but they're not equally good at everything. Understanding where AI excels and where it struggles helps you use it effectively and avoid costly mistakes.
Where AI Excels
Common patterns and idioms. AI has seen millions of examples of standard coding patterns. Need to read a file, parse JSON, or iterate through a list? AI handles these confidently and correctly.
Standard library usage. Questions about built-in functions and common libraries get reliable answers. AI knows how datetime works in Python or how to use Array.map() in JavaScript.
Boilerplate code. Repetitive setup code — configuration files, basic class structures, standard function signatures — is AI's sweet spot. It's seen these patterns countless times.
Syntax questions. "How do I write a for loop in Python?" AI answers these accurately because syntax is well-documented and consistent.
Where AI Struggles
Business logic specific to your problem. AI doesn't know your application's requirements. It might generate plausible-looking code that doesn't actually solve your specific problem.
Security-critical code. Authentication, encryption, and access control require careful attention to detail. AI might miss subtle vulnerabilities that could have serious consequences.
Performance-critical code. AI often generates code that works but isn't optimized. For performance-sensitive applications, you need to verify efficiency yourself.
Recent APIs and libraries. AI's knowledge has a cutoff date. Very new libraries or recent API changes might not be in its training data, leading to outdated or incorrect suggestions.
Math and calculations. Surprisingly, AI makes arithmetic errors. Always verify calculations independently, especially for financial or scientific applications.
Calibrating Your Trust
Use this mental model:
- High trust: "Show me the syntax for X" or "Write boilerplate for Y"
- Medium trust: "Implement this common algorithm" or "Help me debug this error"
- Low trust: "Is this code secure?" or "Will this scale to millions of users?"
For low-trust areas, use AI as a starting point but verify with documentation, testing, and sometimes human experts.