What Is AI Hallucination? A Simple Explanation of Invented Answers
A simple explanation of AI hallucinations, why models produce false or unsupported information, when the risk increases, and how to verify their answers.
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Large Language Models, or LLMs, are AI models trained on large amounts of language data to learn patterns, generate text and perform tasks such as question answering, summarisation and translation.
A simple explanation of AI hallucinations, why models produce false or unsupported information, when the risk increases, and how to verify their answers.
Ask someone to write a short message and you get something generic. Add who it is for and why, and the result changes completely. Same with an AI model.
A simple explanation of tokens, how AI models divide text into smaller units, and how token counts affect context limits and API costs.
Someone telling you a long story starts forgetting the early details. Models have the same limit, and knowing where it sits changes how you work with them.
A friend texts you: can you fix it? Fix what? The sentence alone is not enough. AI has the same problem, and it explains most bad answers you get.
You hear half a sentence and your mind fills in the rest. A language model does something close to that, and it explains both its power and its limits.
I put the book on the table because it was heavy. You knew instantly what it meant. The architecture behind ChatGPT does the same thing, and that is what changed everything.
There is a difference between a multiple choice exam and an essay. Most AI was the first kind. Generative AI is the second, and that changes what you can trust.
What is an LLM and how does it generate text? A simple guide to large language models, how they work, their relationship with ChatGPT and generative AI, and why they sometimes produce incorrect answers.