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Safety
AI Hallucination Causes
Definition
AI hallucinations occur when LLMs generate confident but incorrect information, caused by training data gaps, pattern overgeneralization, and the model's inability to distinguish known from unknown facts.
Why It Matters
Hallucinations are the most common failure mode of LLM applications. Users trust AI outputs, so confident false statements cause real harm. Understanding why hallucinations occur helps you design systems that minimize them and detect them when they happen.
Root Causes
Training-Related:
- Gaps in training data coverage
- Outdated information in training set
- Conflicting information in sources
- Statistical patterns without semantic understanding
Generation-Related:
- Pressure to produce fluent output
- No mechanism to express uncertainty
- Pattern completion overriding factual recall
- Long-range context degradation
Mitigation Strategies
- RAG: Ground responses in retrieved documents
- Citations: Require sources for factual claims
- Confidence Calibration: Ask model to rate its certainty
- Verification: Cross-check critical facts
- Scope Limiting: Constrain to known domains
- User Education: Set appropriate expectations