Dev Tools · 1h ago
LLM Filter Prefers Silence Over Slop With Eval Harness
A developer built a pipeline that filters AI-generated content by prioritizing low false-positive rates over recall. The system uses a chain of gates including hard rules, model abstention, and post-veto checks to reject uncertain outputs. An eval harness with hand-labeled data ensures the filter's performance is measurable, not just a vibe.
Meridian48 take
The emphasis on eval harnesses over model tweaks is the real insight — without a golden dataset, a silent filter is indistinguishable from a broken one.
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I Built an LLM Filter That Prefers Silence Over Slop — and the Eval Harness That Keeps It Honest →
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llm-filteringeval-harness