Why Does AI Hallucinate?

AI hallucinations refer to responses that sound convincing but are factually incorrect or fabricated, such as fictional publications or erroneous representations of existing research. This phenomenon can be difficult to recognize because results often seem plausible but lack important details. A recent study from OpenAI, published in September 2025, provides insight into the underlying mechanisms of these hallucinations, related to the training and assessment methods of AI systems.

A crucial aspect of the problem is the comparison to a test where gambling can yield more than giving an unconfirmed answer. When systems evaluate based solely on correctness and do not penalize incorrect answers more strictly, a model will be inclined to gamble to achieve better scores. The research advocates for a reevaluation of assessment criteria, where confident inaccuracies should be penalized more heavily and appropriate uncertainty may receive more appreciation.

As a user of AI tools, it is essential to maintain a critical eye on the provided answers. It is advisable to check the origin of significant statements, for example, by asking for the original source and verifying it yourself. Specific instructions can help steer the expected results, although this approach also does not guarantee flawless outputs. Defining responsibilities within organizations is also important; who is responsible for the review and approval of AI-generated information?

A useful technique to promote accuracy is the application of fact-check prompts. By encouraging the AI system to critically review its previous answers, errors and missing sources can be identified. This includes systematically checking claims using original sources so that users can make better-informed decisions based on corrective information. These methods can help mitigate the risks of AI hallucinations and ensure a more reliable application of technology in business contexts.

Read the full article from AI Insider.