Why AI Keeps Making Stuff Up—And How to Fix It

Hallucinations in large language models (LLMs) are not mere glitches but structural flaws rooted in their training mechanisms. OpenAI's research reveals that these models prioritize confidence over accuracy, often fabricating answers rather than admitting ignorance. The solution may lie in recalibrating scoring systems to reward honesty—such as saying "I don't know"—instead of penalizing uncertainty.

Users can mitigate this issue by demanding sources, crafting precise prompts, and leveraging fact-checking features. The current system, akin to an exam graded on partial credit, incentivizes guesswork. Without reform, LLMs will continue to produce polished but potentially false responses, as the mathematical framework of their optimization penalizes hesitation.