A short guide to managing generative AI hallucinations

Generative AI systems occasionally produce false or misleading information, a phenomenon known as hallucination. This issue is becoming more significant as enterprises increasingly rely on AI for their information and data-rich applications.

AI hallucinations can range from annoying inaccuracies to severe business disruptions. When AI systems output false information, it can erode an organization's integrity and result in costly and time-consuming repairs.

To reduce the risk of AI-generated misinformation and enhance system reliability, AI practitioners must understand, identify and mitigate potential hallucination concerns.

What are generative AI hallucinations? Generative AI hallucinations occur when the AI model produces incorrect, misleading or wholly fabricated information. This phenomenon can arise across various generative AI systems, including text generators, image creators and more. Hallucinations are typically unintended, stemming from generative AI's reliance on patterns learned from its training data rather than access to external factual databases or real-time information. This reliance can lead to outputs that, while superficially plausible or coherent, are not anchored in reality. Addressing these hallucinations is a significant challenge in AI development. A typical generative AI hallucination contains fabricated facts, such as incorrect historical events or fictional scientific data. For example, when creating a custom GPT to generate instructions for a software feature, a hallucination might occur if the model produces command-line instructions that do not work when run. In text-based models like large language models, hallucinations can manifest as factually inaccurate content, false attributions and nonexistent quotes. In image-generating AI, hallucinations involve the creation of images with distorted or unrealistic elements.