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    Hallucinations are still a major blocker for deploying reliable retrieval-augmented generation (RAG) systems, especially in complex domains like medical or legal. Most existing hallucination detectors rely on full LLM inference (expensive, slow), or struggle with long-context inputs. I built LettuceDetect — an open-source, encoder-only framework that detects hallucinated spans in LLM-generated answers based on the retrieved context. No LLMs needed, and it much more efficiently. Highlights: - Token-level hallucination detection (unsupported spans flagged based on retrieved evidence) - Built…

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