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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 its own response, simulating a meta-cognitive check. If the model determines that its confidence is low, we automatically trigger a web research phase that retrieves relevant information from live sources and synthesizes a more reliable answer. By combining confidence estimation, self-reflection, and automated research, we create a practical framework for building more trustworthy and transparent AI systems that can recognize uncertainty and actively seek better information.
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- Incude `channel_id` and `retrieved_at` in the database schema.
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