Unverified60% confidenceFactExact time
When a prompt is entered, the model predicts the most likely next token based on context, rather than truly understanding the semantics.
2
Sources
60%
Confidence
Long-term
Relevance
7/7/2026
First Seen
Sources
提示词工程入门：核心概念、方法论与局限性完全解析
bilibili大模型学习教程7/6/2026
Related Claims
UnverifiedThe autoregressive generation mechanism of language models is essentially predicting the next token based on local context, lacking a persistently effective explicit goal representation that spans multiple rounds of interaction.79% similarUnverifiedThe model does not truly remember information across sessions; it can only make probabilistic inferences based on content within the current context window.78% similarVerifiedThe core mechanism of large model inference is probability-based Next Token Prediction, generating complete output word by word.77% similarUnverifiedAfter the model in Goal mode receives the prompt, it only estimates what to do in the first round; after completing and verifying that round, it then checks whether the task is finished, and the total number of rounds is unknown in advance.76% similarUnverified模型基于前文预测后文，Prompt中任何一个字的变化都会改变后续生成概率，而标点符号的影响权重较小76% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/168769API
curl https://kongchang.com/api/v1/knowledge/claims/168769MCP
get_claim(id=168769)