Artificial intelligence models' propensity for generating inaccurate or misleading information may stem from training methodologies that prioritize perceived helpfulness over factual correctness. Researchers suggest that these techniques, designed to make AIs appear more responsive and engaging, inadvertently incentivize the production of plausible-sounding but ultimately false or unsubstantiated claims. This highlights a critical challenge in AI development: balancing user experience with the need for reliable and trustworthy information.