Verified65% confidenceOpinionTime unknown
Incremental Prompting can reduce hallucinations and logical errors in large language models when processing complex instructions
3
Sources
65%
Confidence
Long-term
Relevance
6/3/2026
First Seen
Sources
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Related Claims
Unverified利用diff而非全文替换来驱动代码修改可减少token消耗并降低模型幻觉引发大范围错误的风险79% similarUnverifiedLarge language models are prone to hallucinations when handling complex and ambiguous requirements; the larger and more ambiguous the scope, the higher the probability of AI errors, so reducing scope for MVP lowers the risk of AI mistakes.77% similarUnverifiedLarge language models are prone to hallucination drift when handling detail consistency problems that require precise long-range context tracking.76% similarUnverifiedWhen faced with ambiguous instructions, large models tend to fill information gaps with content that appears plausible but is unverified (hallucination completion), and explicitly writing out constraints and acceptance criteria can suppress this completion impulse.76% similar
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