HaluGate is a real-time hallucination detection system for production LLMs that identifies when models generate claims contradicting provided context. It uses a two-stage pipeline: first classifying whether queries need fact-checking (96.4% accuracy, 12ms latency), then performing token-level detection with NLI explanation for
Table of contents
The Problem: Hallucinations Block Production DeploymentThe Scenario: When Tools Work But Models Don’tThe Insight: Function Calling as Ground TruthWhy Not Just Use LLM-as-Judge?HaluGate: A Two-Stage Detection PipelineIntegration with Signal-Decision ArchitectureResponse Headers: Actionable TransparencyThe Complete Pipeline: Three PathsModel Architecture Deep DiveWhy Native Rust/Candle MattersConfiguration ReferenceBeyond Production: HaluGate as an Evaluation FrameworkLimitations and ScopeAcknowledgmentsConclusion1 Comment
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