Hallucination

Hallucination is when a language model generates text that sounds confident and plausible but is factually wrong—invented citations, fabricated statistics, nonexistent API endpoints. It happens because LLMs are not databases. They are pattern-completion engines that predict likely next tokens, and sometimes the likeliest continuation is a fluent lie. Hallucination rates vary by model, task, and domain: open-ended creative writing has different tolerances than legal research. Mitigation strategies include retrieval-augmented generation (grounding responses in source documents), chain-of-thought prompting (forcing the model to show its reasoning), and structured output validation. None of these eliminate hallucination entirely. Any system where an LLM's output reaches a customer, a contract, or a database without human review or automated verification is a system waiting to embarrass you.