<\/span><\/h3>\n\n\n\nHallucinations occur when an AI model generates information that isn’t grounded in facts. This happens for three reasons:<\/p>\n\n\n\n
Lack of constraints.<\/strong> You didn’t set boundaries, so the model filled the gaps with plausible-sounding nonsense.<\/p>\n\n\n\nLack of grounding.<\/strong> You didn’t provide enough context, so the model invented what it needed.<\/p>\n\n\n\nOverly broad instructions.<\/strong> You asked for “everything about X,” so the model generated generic filler to meet your request.<\/p>\n\n\n\nHallucinations aren’t random. They’re predictable responses to unclear inputs.<\/p>\n\n\n\n
<\/span>The Three Causes of Hallucination You Can Control<\/span><\/h3>\n\n\n\n1. Missing info.<\/strong> You left out critical details, so the model guessed. If you ask “Write a report on sales,” the model has to invent which sales, which period, and which format.<\/p>\n\n\n\n2. Ambiguous request.<\/strong> You asked for something vague\u2014”Write something persuasive”\u2014so the model defaulted to patterns in its training data, which may not match your intent.<\/p>\n\n\n\n3. Wrong type of task.<\/strong> You asked the model to do something it’s not designed for. Asking for precise financial data or obscure historical facts invites hallucination because the model prioritises fluency over accuracy.<\/p>\n\n\n\n