Unverified50% confidenceOpinionExact time
Clearer and more structured prompts can reduce extra token consumption caused by repeated corrections
1
Sources
50%
Confidence
Long-term
Relevance
7/24/2026
First Seen
Sources
高中生用Codex+GPT做ToDo应用：15元AI编程初体验
Related Claims
Unverified结构化、上下文丰富的提示不仅提升模型输出质量，也能显著降低安全分类器的误触发率72% similarUnverifiedSub-agents with narrower task scopes require smaller context windows, directly reducing inference costs and lowering error rates caused by context contamination.68% similarUnverifiedCompared to the polling model, an event-driven asynchronous architecture can achieve an order-of-magnitude reduction in token consumption.68% similar
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