The study addresses the issue of hallucinations in Retrieval-Augmented Generation (RAG) systems, where Large Language Models (LLMs) may generate incorrect responses not supported by retrieved context. Evaluating popular hallucination detectors across four public RAG datasets, the research highlights various LLM-based techniques, including RAGAS, G-Eval, DeepEval's hallucination metric, and Trustworthy Language Model (TLM), for their ability to identify and flag erroneous outputs. TLM consistently outperforms other methods, demonstrating superior precision and recall in detecting hallucinations, which is crucial for high-stakes applications in fields like finance and medicine. Despite the promise of these detection methods, challenges remain, particularly with datasets requiring complex reasoning. The findings emphasize the need for robust detection frameworks to ensure trustworthy RAG outputs, with TLM offering a viable solution to enhance the reliability of enterprise AI systems.