Large language models (LLMs) are impressive, but they sometimes 'hallucinate,' meaning they confidently generate incorrect or nonsensical information. This poses a significant challenge for real-world applications where accuracy is paramount. How can we measure and ultimately mitigate these hallucinations? Researchers have introduced ANAH, a new method for analyzing and annotating hallucinations in LLMs. Unlike previous approaches that simply labeled outputs as hallucinatory or not, ANAH provides a fine-grained, sentence-by-sentence analysis. Imagine a detective meticulously examining each statement an LLM makes, cross-referencing it with reliable sources. That's essentially what ANAH does. It retrieves supporting evidence for each sentence generated by the LLM, categorizing it as either accurate, contradictory, unverifiable, or simply lacking factual content. This detailed analysis allows researchers to pinpoint exactly where an LLM goes off track, providing valuable insights into the nature of hallucinations. ANAH was used to train specialized 'hallucination annotators,' AI models designed to automatically detect and categorize hallucinations. The results are promising: these annotators can achieve accuracy comparable to human experts, offering a scalable solution for identifying and correcting LLM errors. The research also revealed a 'snowball effect' with hallucinations: once an LLM starts hallucinating, it's more likely to continue doing so in subsequent sentences. This highlights the importance of early detection and correction. ANAH represents a significant step towards making LLMs more reliable and trustworthy. By providing a detailed understanding of how and why LLMs hallucinate, ANAH paves the way for developing more robust and accurate language models in the future. The ability to catch and correct these AI hallucinations is crucial for building applications we can truly rely on, from chatbots to medical diagnosis tools. ANAH brings us closer to that goal.