待验证50% 置信事实精确时间
LLM outputs are probabilistic rather than deterministic, and prompt wording can significantly affect output quality
1
来源数
50%
置信度
中期 (~90 天)
时效性
2026/7/2
首次发现
有效期至：2026/9/30
来源
Java Developer's Guide to AI: Hands-On with Spring AI and RAG
bilibiliOpencv学习教程2026/6/24
相关事实
待验证使用 LLM 编程时，提供正确的上下文比打磨提示词措辞更能提升输出质量77% 相似待验证LLMs are fundamentally probabilistic text generators that predict the next most likely token rather than retrieving answers from a factual database.70% 相似待验证LLMs essentially perform probability predictions for the most likely next word rather than creating based on deep understanding of brand, audience psychology, and business objectives70% 相似已验证LLM的生成机制是基于概率的文本预测，而非真正'理解'代码语义70% 相似已验证LLM的输出具有天然的非确定性，即使输入相同的Prompt，模型在不同时刻可能返回格式、内容甚至逻辑不同的结果69% 相似
引用此条事实
Stable URI
https://kongchang.com/claim/41115API
curl https://kongchang.com/api/v1/knowledge/claims/41115MCP
get_claim(id=41115)