Expired50% confidenceFactTime unknown
Every character in a prompt affects the probability distribution of all subsequent tokens, which is why subtle wording changes can lead to drastically different outputs.
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: Principles, Techniques, and Complete Practical Guide
bilibiliAgent搭建教程
Related Claims
UnverifiedThe output of LLMs is inherently stochastic, and identical inputs may produce entirely different results across different calls.74% similarUnverifiedIn few-shot prompting, simply changing the order of examples can cause dramatic fluctuations in accuracy, ranging from near-random to near-optimal.73% similarUnverified在自回归生成中,随着文本长度增加,早期设定的约束条件在概率分布中的权重逐渐降低,导致生成内容偏离原始意图72% similarUnverified语义相近但措辞不同的提示在同一模型上可能产生截然不同的输出质量,这一现象被称为提示敏感性(Prompt Sensitivity)70% similarUnverifiedGreedy decoding selects the token with the highest current probability at every step, which tends to accumulate systematic bias in multi-step tasks because local optima do not equal global optima.69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/58677API
curl https://kongchang.com/api/v1/knowledge/claims/58677MCP
get_claim(id=58677)