The hardest LLM mistakes to catch are the ones that look perfectly reasonable
Innowise_ · reddit · 2026-09-30
The most worrying AI coding mistakes aren't the obvious ones — invented APIs, nonsense output, code that doesn't run are found quickly. The hard cases are outputs that look completely reasonable:
- Sound architecture that ignores one constraint
- SQL that works but breaks at real data volume
- Retry logic that isn't idempotent
- Access-control logic that misses an edge case
Nothing looks visibly broken; you need enough context to realize an assumption is wrong. You can test if code runs, check types, run static analysis, add evals — but catching AI output that is technically valid, logically coherent, and still wrong for the target system remains an open problem.
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