| name | failure-taxonomy |
| description | Classifying AI failures — hallucination, refusal, irrelevance, tone mismatch, latency. |
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
- Context loss: The AI loses track of conversation history
Safety Failures:
- Harmful content: The AI generates content that could cause harm
- Privacy violation: The AI reveals sensitive information
- Bias manifestation: The AI's output shows bias against a group
- Manipulation: The AI's output could be used to deceive or manipulate
Severity Levels
- Critical: Causes harm or creates serious trust violation. Requires immediate fix.
- High: Significantly degrades user experience or task success. Fix within days.
- Medium: Noticeable quality issue that users can work around. Fix within weeks.
- Low: Minor quality issue. Track and batch with other fixes.
Using the Taxonomy
- Logging: Classify every detected failure by type and severity
- Trending: Track failure type frequency over time
- Prioritisation: Address highest-severity, highest-frequency failures first
- Root cause analysis: Group failures by type to identify systemic causes
- Prevention: Use failure patterns to inform guardrail design and prompt improvements
Design Artefacts
- Failure taxonomy reference document
- Failure logging templates
- Severity classification rubric
- Failure trend dashboards
- Root cause analysis protocols
