Expired50% confidenceFactExact time
The most fundamental principle of AI large language models is generating the next token based on probability
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 9/30/2026(expired)
Sources
Core Methodology of Prompt Engineering: A Systematic Deep Dive from Principles to Practice
bilibili大模型研究所5/27/2026
Related Claims
UnverifiedLanguage models work through autoregressive generation: given all preceding tokens, they predict the probability distribution of the next token and sample an output.76% similarVerifiedThe randomness of AI large language model outputs stems from the temperature parameter and sampling strategy; when generating each token, the model is essentially performing probabilistic sampling.75% similarUnverifiedWhen generating each token, large language models perform probabilistic sampling over all possible next words in the vocabulary, rather than deterministically selecting a unique answer.74% similarUnverifiedThe core mechanism of large model inference is probability-based Next Token Prediction, generating complete output word by word.73% similarVerifiedThe root cause of AI hallucination lies in the generative mechanism of LLMs: the model is essentially performing next-token probability prediction, optimizing for linguistic coherence rather than factual accuracy.72% similar
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