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Dev tools · November 23, 2025

Hallucination Detector

Real-time Hallucination Detection for LLMs

Visit site ↗Alternativestop 12% of November 2025

What it does

Hallucination Detector is a system that goes beyond basic fact-checking, using statistical analysis and weighted scoring to identify false or unsupported outputs from LLMs. It provides real-time detection, confidence scoring, coverage across 50+ domains, and model performance tracking. Errors are classified by severity, and accuracy improves through statistical accumulation. With dual-axis scoring, precise risk weights, and trust dampening, it delivers higher reliability and AI transparency.

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    Hi all! This morning, we released a new Apache 2.0 licensed model on HuggingFace for detecting hallucinations in retrieval augmented generation (RAG) systems. What we've found is that even when given a "simple" instruction like "summarize the following news article," every LLM that's available hallucinates to some extent, making up details that never existed in the source article -- and some of them quite a bit. As a RAG provider and proponents of ethical AI, we want to see LLMs get better at this. We've published an open source model, a blog more thoroughly describing our methodology (and…

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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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