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Hallucination

As explained in Inside AI

A hallucination is AI-generated content that is nonsensical, unsupported, or false despite appearing to be a meaningful response.

A hallucination is AI-generated content that appears to answer a question but is false, unsupported, or incoherent. A fluent response is not necessarily an accurate one. section 5.12 section 5.8

Different answers to the same question

Asked “Who is Akli Adjaoute?” in fresh sessions, ChatGPT produced different false biographies. It attributed an unrelated company, an award, and a PhD to him. The fifth attempt gave an accurate answer. Repeating a prompt did not make the earlier claims reliable. section 5.3.2

ChatGPT generates words in response to input; predicting plausible word combinations does not guarantee factual accuracy. False or biased input data can also influence LLM output. These limitations matter when generated answers are used to seek knowledge online or teach students: unchecked claims can displace testable sources. Check important factual claims against such sources rather than trusting an answer because it sounds convincing. section 5.3.2 section 5.10 section 5.8

How Inside AI covers it

How deep it goes
Defines inaccurate generated content and illustrates it with repeated factual errors, without detailing a detection procedure.
Examples
Recounts fresh ChatGPT sessions that produced several invented biographies before an accurate answer on the fifth attempt.
What it assumes
Introduces the term through a familiar factual question and explains the accuracy problem without assuming knowledge of model internals.
What you can do after
Evaluate/understand it by checking generated factual claims against testable sources instead of accepting plausible answers at face value.
How it connects
Connects hallucination to large language models, deep learning, input quality, and disinformation.

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