How to Detect Hallucinations in RAG Systems Using RAGAS and Other Tools

In Retrieval-Augmented Generation (RAG) systems, hallucination occurs when a large language model generates a response from its own training data rather than from the provided document context. One detection approach involves comparing the vector embeddings of the model's response with those of the retrieved context to measure semantic closeness. LettuceDetect, a BERT-based model, can identify potential hallucinations at the character level by analyzing responses word by word. RAGAS, an evaluation framework, offers multiple metrics — including Faithfulness, Answer Relevancy, and Context Recall — to systematically assess RAG output quality and can be integrated into CI/CD pipelines for automated testing. DeepEval is another available tool that can similarly be used to evaluate RAG systems and flag hallucinated responses.