How I Built a Hallucination Detector for RAG Pipelines in Python

How I Built a Hallucination Detector for RAG Pipelines in Python Every developer who has shipped a RAG application knows this moment. You retrieve the right documents. You pass them to the LLM. The response comes back confident, well-structured, and fluent. You ship it. Then a user reports that the LLM cited a statistic that wasn't in any of your documents. Or named a person who doesn't exist. Or described a process that contradicts your source material — while sounding completely authoritative. This is hallucination in a RAG pipeline. And it is surprisingly hard to catch systematically. I built HallucinationBench to solve this in the simplest way possible. The core idea The approach is straightforward: use GPT-4o-mini as a structured judge. Given a context (your retrieved documents) and a response (the LLM's output), the judge: Breaks the response into individual factual claims Classifies each claim as grounded (supported by context) or hallucinated (absent or contradicted) Returns a