AI 'hallucinations' occur because the technology functions like a super-powered auto-complete system, predicting likely words rather than retrieving facts.

Large Language Models (LLMs) like GPT-4 and Claude are built on transformer architectures that use a process called Next Token Prediction. Research from MIT, Stanford, and other institutions shows these models don't possess an internal world model or library of verified facts. Instead, they calculate the statistical probability of the next word, or "token," appearing in a sequence based on billions of parameters learned during training.The term "hallucination" gained widespread use around 2022 as users observed AI systems generating convincing but fabricated citations and facts. This happens because the model's objective is to minimize prediction error in language generation, not to maximize factual accuracy. When training data contains gaps or inconsistencies, the model fills those voids with the most statistically probable linguistic structure, regardless of whether the content is true.A 2023 study by researchers at Hugging Face found that hallucination rates vary significantly based on prompt complexity. The phenomenon gained public attention through high-profile failures, such as the 2023 Mata v. Avianca case, where a lawyer used ChatGPT to draft a legal brief containing six entirely fabricated court decisions. This case exemplifies the "stochastic parrot" theory—the idea that AI systems mimic human language patterns without genuinely understanding the factual reality behind the words they generate.