Large language models such as OpenAI’s GPT-4 have recently become a subject of interest for their use in education and information exchange. Such applications are hampered by the LLM’s capacity to produce superficially plausible but ultimately incorrect information, called hallucinations. Using a bank of trivia questions sourced from the 1981 Genus Edition of Trivial Pursuit, we conducted a series of trials on GPT-4o Mini to measure the accuracy and consistency of the model and the characteristics of its hallucinations. The model demonstrated impressive accuracy across trials, averaging approximately 85%. Our results reinforced prior research, showing that the model’s output is effectively non-deterministic and that its answers to identical questions can vary across sessions, meaning correct information cannot always be trusted to persist. We find evidence that GPT’s demonstrated ability to improve incorrect answers when prompted is a consequence of random chance, that the model is more likely to break correct answers than fix incorrect ones, and that the strength of wording affects how likely this change is. We show that the model’s factual knowledge is not discrete but probabilistic, with some questions being more prone to hallucination and the model’s consistency being a reasonable metric of confidence in the information provided. Finally, we use the results to recommend best practices for future research examining the model’s performance in professional and educational environments.