Developer builds LLM news filter that abstains when uncertain to cut false positives

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A developer built a financial news filtering pipeline designed to prioritize silence over forwarding incorrect information, after observing that confidently wrong AI outputs posed real risks to decision-makers. The system uses a multi-stage chain of gates, starting with text normalization and hard regex rules, before passing items to a language model that is explicitly allowed to return an 'uncertain' verdict, which the pipeline treats as a rejection. Even confident model approvals are subject to a post-veto step that cross-checks any numbers or entities cited in the model's reasoning against the original source text. Because a low false-positive filter is quiet whether it is working or broken, the developer built an evaluation harness using a hand-labeled golden dataset to continuously measure performance. The project argues that for high-stakes filtering tasks, recall is an acceptable trade-off, and that an unmeasured filter is little more than a guess.

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