Large language models (LLMs) are impressive, but they sometimes hallucinate—making up facts or getting details wrong. This poses a big problem for trust and reliability. New research explores how to make LLMs more factual by teaching them to essentially fact-check themselves. The approach, called Atomic Fact Decomposition-based Retrieval and Editing (ARE), works by breaking down an LLM's initial answer into smaller, atomic facts. Then, the system uses a search engine to find evidence supporting each fact. An "evidence verifier" checks if the facts are supported, need editing, or are irrelevant. If a fact needs revision, the system uses retrieved evidence to correct it. Irrelevant facts get expanded and re-searched. Finally, the edited atomic facts are reassembled into a revised, more accurate answer, complete with an attribution report linking claims to evidence. This granular approach allows for precise adjustments without overhauling the entire response, preserving the original intent while boosting accuracy. Experiments show ARE significantly improves both the quality of attributions and the factual accuracy of answers across diverse question-answering datasets, boosting trustworthiness compared to previous methods. This type of self-verification could be crucial for deploying LLMs in real-world applications where reliability is paramount, like journalism, education, and customer service. While promising, challenges remain. Search engines aren't perfect, and the verification process itself relies on LLMs, which can still make mistakes. Future work might explore more robust verification methods and better ways to handle complex reasoning or ambiguous information. This research offers a glimpse into how we might build more trustworthy and transparent AI systems that can justify their claims with real evidence.