What is AI Hallucination Mitigation

Definition

AI hallucination mitigation is the set of techniques used to reduce the frequency and impact of fabricated or unsupported AI outputs, including grounding with retrieval, requiring citations, constraining outputs, and adding verification so responses stay accurate and trustworthy.

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Reduces fabricated, unsupported AI outputs through grounding
Improves trustworthiness for high-stakes AI applications
Adds verification and citation so claims can be checked
Lowers the risk of acting on false AI-generated information

Real World Example

A team mitigates hallucination in its assistant by grounding answers in retrieved documents, requiring source citations, and adding a verification step that withholds claims the sources do not support.

FAQs

How is hallucination mitigated?

Through retrieval grounding, requiring citations, constraining outputs, verification checks, and human review.

Can hallucination be eliminated entirely?

Not completely, but mitigation greatly reduces its frequency and the risk of acting on false output.

Why does grounding help?

Anchoring responses to retrieved source data keeps the model from inventing unsupported facts.