AI hallucinations, occurring in generative AI models like GPT and Claude, manifest when these systems produce plausible yet incorrect outputs. They are categorized into intrinsic—and extrinsic hallucinations. Intrinsic refers to misunderstandings of input, while extrinsic represents complete fabrications. These errors can arise in various formats and industries—healthcare, finance, and law, making them particularly concerning without proper oversight. Taming this issue requires integrating Human-in-the-Loop Testing to ensure the accuracy of AI outputs and mitigate potential misinformation risks.
AI hallucinations occur when an artificial intelligence system generates incorrect or misleading outputs based on patterns that don't actually exist.
These hallucinations can surface in text, images, audio, or decision-making processes, and they present risks without proper oversight.
Collection
[
|
...
]