An organization recently introduced a generative AI chatbot that can interact with users and answer their queries. Which of the following would BEST mitigate hallucination risk identified by the risk

The correct answer is D. Fine-tuning the foundational model. AAISM highlights fine-tuning foundational models as one of the most effective strategies for reducing hallucination risk. By tailoring the model with domain-specific, curated, and verified datasets, organizations can reduce the frequency of irrelevant or fabricated outputs. Testi

AI Security Risk Management

Question

An organization recently introduced a generative AI chatbot that can interact with users and answer their queries. Which of the following would BEST mitigate hallucination risk identified by the risk team?

Options

APerforming model testing and validation
BTraining the foundational model on large data sets
CEnsuring model developers have been trained in AI risk
DFine-tuning the foundational model

How the community answered

(30 responses)

A
13% (4)
B
3% (1)
C
7% (2)
D
77% (23)

Explanation

AAISM highlights fine-tuning foundational models as one of the most effective strategies for reducing hallucination risk. By tailoring the model with domain-specific, curated, and verified datasets, organizations can reduce the frequency of irrelevant or fabricated outputs. Testing and validation help evaluate risks but do not directly minimize hallucinations. Training on larger datasets may improve generalization but does not guarantee accuracy. Developer training in AI risk supports governance but is not a technical control against hallucinations. The best mitigation is fine-tuning to align the chatbot with trusted, context-specific knowledge.

Topics

#Generative AI#Hallucination risk#Model fine-tuning#Risk mitigation

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