Testing & Edge Cases

This section focuses on how AI systems behave under real-world conditions — not ideal inputs.

What this covers

• Edge cases (unclear, conflicting, incomplete inputs)
• Failure modes and fallback logic
• Prompt robustness under stress
• System reliability

Why it matters

In production:

• inputs are messy
• users are inconsistent
• data is incomplete

Without testing:

• systems fail unpredictably
• outputs become unreliable
• automation breaks

What to look for

• How the system handles ambiguity
• Whether it asks for clarification or guesses
• How failures are managed
• How consistency is maintained

Key principle

AI systems are judged by their worst-case behavior, not their best-case output.