Unverified50% confidenceTradeoffExact time
The difficulty of refined prompt optimization lies in reducing word count without losing critical instruction information, as doing so would affect output quality.
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50%
Confidence
Long-term
Relevance
9/26/2026
First Seen
Sources
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Related Claims
UnverifiedLong prompts suffer from execution decay: the more constraints there are, the greater the deviation between actual output and textual description, and a more detailed prompt does not equal more accurate model execution.76% similarUnverifiedDeterministic tasks involving numbers should be written into scripts rather than relying on prompts, which yields lower token consumption and greater output stability.75% similarUnverifiedFeeding large amounts of irrelevant code to a model dilutes key information and degrades reasoning quality, so even with large context windows, precise context selection remains critical.75% similarUnverifiedColibri makes the following commitments: the default policy will not silently alter progress or routing, speed will only be reduced rather than accuracy when high-speed memory is insufficient, and speculative decoding must be proven worthwhile or it will be disabled.74% similar
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