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从实际案例来看,In conclusion, we designed and implemented a complete uncertainty-aware reasoning pipeline for large language models using Python and the OpenAI API. We demonstrated how models can verbalize confidence, perform internal self-evaluation, and automatically conduct research when uncertainty is detected. This approach improves reliability by enabling the system to acknowledge knowledge gaps and augment its answers with external evidence when needed. By integrating these components into a unified workflow, we showed how developers can build AI systems that are intelligent, calibrated, transparent, and adaptive, making them far more suitable for real-world decision-support applications.

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徐丽,独立研究员,专注于数据分析与市场趋势研究,多篇文章获得业内好评。