Failure Taxonomy

Not all AI failures are the same. A hallucination is different from a refusal, which is different from a tone mismatch. A failure taxonomy classifies failure types so teams can track, prioritise, and address them systematically.

Failure Categories

Content Failures:

• Hallucination: The AI presents false information as fact
• Inaccuracy: The AI gets details wrong (dates, numbers, names)
• Incompleteness: The AI misses important information
• Irrelevance: The AI's response doesn't address the user's actual question
• Contradiction: The AI contradicts itself within or across responses Behavioral Failures:
• Inappropriate refusal: The AI refuses a reasonable request
• Missing refusal: The AI fulfils a request it should have declined
• Tone mismatch: The AI's tone is wrong for the context
• Persona break: The AI drops out of its defined persona
• Over-generation: The AI produces far more than needed Technical Failures:
• Latency: Response takes too long
• Truncation: Response is cut off
• Format errors: Output is in the wrong format or structure
• Tool failures: The AI attempts to use a tool and fails