Large language models (LLMs) are revolutionizing how we access information, offering a conversational alternative to traditional search engines. However, these impressive AI systems sometimes generate responses that sound plausible but are factually incorrect—a phenomenon known as "hallucination." This tendency to fabricate information undermines trust and poses a significant challenge for reliable information retrieval. A groundbreaking research paper introduces a new approach to combat these AI hallucinations called Dynamic Retrieval Augmentation based on Hallucination Detection (DRAD). The core of DRAD lies in its two-pronged approach: Real-time Hallucination Detection (RHD) and Self-correction based on External Knowledge (SEK). RHD acts as a vigilant watchdog, constantly monitoring the LLM's output for signs of hallucination. It does this by analyzing the uncertainty associated with generated entities, flagging low-probability or high-entropy terms as potentially fabricated. When RHD detects a potential hallucination, SEK kicks in. It formulates a targeted query based on the surrounding context and retrieves relevant information from an external knowledge base, such as Wikipedia. This external knowledge is then fed back to the LLM, allowing it to self-correct and generate a more accurate response. The results are impressive. DRAD not only significantly reduces hallucinations but also does so more efficiently than previous methods, requiring fewer calls to external databases. This efficiency is crucial for maintaining the speed and responsiveness of these conversational AI systems. While this research marks a significant step forward, challenges remain. One limitation of RHD lies in detecting hallucinations stemming from incorrect information learned during the LLM's training, as opposed to gaps in knowledge. In such cases, the LLM may confidently generate wrong answers, making them harder to detect. Future research aims to address this by exploring alternative detection methods that look beyond uncertainty. This ongoing research is paving the way for more trustworthy and reliable conversational AI, bringing us closer to a future where we can confidently rely on AI for accurate and informative responses.