How AI silently fills code gaps when requirements are ambiguous

When AI tools generate code from vague specifications and limited examples, they quietly make undocumented assumptions to fill logical gaps — without flagging them. Engineers at knopfdruck.ai discovered this after realising their AI had implemented calculation rules that differed from what was actually intended, even though all tests passed. The core problem is that a brief description plus sample data does not define a single rule, but a family of rules that all fit the samples equally well. Their fix was to require the AI to articulate its interpretation in plain prose before writing any code, iterating until the AI's understanding matched the intended logic. Tests are then built from the agreed rule rather than just the original samples, ensuring edge cases are covered and hidden decisions are surfaced early.