Large Language Models (LLMs) have revolutionized various industries. However, the phenomenon of hallucinations continues to challenge reliability. To address this issue, a combination of detection mechanisms, explainability tools, and robust mitigation strategies are required. Innovations such as Retrieval-Augmented Generation (RAG) systems and evaluation strategies such as the Retrieval-Augmented Generation Assessment Strategy (RAGAS) offer a promising path toward reducing hallucination risks. Advanced techniques for hallucination detection, ranging from entropy-based uncertainty estimators to model-graded evaluations, are being developed to identify confabulations. Explainable AI (XAI) tools provide insights into model limitations and help trace the origins of the unsupported outputs. RAG systems integrate advanced retrieval mechanisms with powerful generative models to ensure that AI outputs are grounded in factual information. The implementation of effective RAG systems requires a focus on data quality, chunking strategies, and advanced retrieval techniques. Optimizing retrieval, seamlessly integrating retrieved information, fine-tuning generative models, and building strong guardrails are essential for the success of RAG. RAGAS provides a multidimensional framework for evaluating RAG systems, focusing on relevance, accuracy, grounding, answerability, and style. Future research must focus on scalable, real-time hallucination mitigation strategies, incorporating multimodal capabilities, and adapting evaluation frameworks such as RAGAS. The synergy between RAG systems and RAGAS represents a paradigm shift in addressing hallucinations, thereby enabling more robust, trustworthy, and impactful AI systems.