AI models operate by predicting likely next words, not by understanding content.

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A transformer-based AI processes prompts by converting words into numerical coordinates within a semantic space. It then uses attention mechanisms to determine which words influence its response, generating outputs by sampling from a probability distribution one token at a time. The system is designed to produce statistically plausible text, not to verify facts or reason logically. This fundamental mechanism explains behaviors like mathematical errors, hallucinations, and why prompt engineering techniques can shape its outputs.

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