In this tutorial, we build an uncertainty-aware large language model system that not only generates answers but also estimates the confidence in those answers. We implement a three-stage reasoning pipeline in which the model first produces an answer along with a self-reported confidence score and a justification. We then introduce a self-evaluation step that allows the model to critique and refine...
The article represents a constructive example of AI development and analysis. The uncertainty-aware reasoning pipeline is designed to improve the transparency and trustworthiness of AI systems by allowing them to acknowledge their limitations and seek external evidence when necessary. This approach aligns with the A.R.C. Codex principles of intellectual rigor, humanist clarity, and collaborative engagement.
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Root Cause: The development of the uncertainty-aware reasoning pi...