Large language models (LLMs) are impressive, but they have a problem: they sometimes 'hallucinate,' meaning they make things up. This isn't just about getting facts wrong—it's about building trust in the age of AI. Researchers are tackling this challenge head-on. A new approach focuses on building an AI 'fact-checker' called FENCE (Fine-grained Critique-based Evaluator). FENCE doesn't just give a thumbs up or down to what an LLM says. It provides specific feedback, like pointing out where the LLM went wrong and suggesting corrections. Even more impressive, FENCE uses multiple sources to verify information. Think of it as an AI detective using a combination of internet searches, encyclopedias, and knowledge databases. This gives FENCE a broader perspective, reducing the chances of being fooled by the LLM’s convincing but false statements. This research goes beyond just checking facts. It’s about teaching the LLM to learn from its mistakes. By using FENCE’s feedback, researchers have found that LLMs can become significantly more accurate, even up to 14.45% better at generating factual biographies. Interestingly, they also noticed LLMs become more cautious, refusing to answer questions about topics they haven't learned enough about. This 'AI humility' is a crucial step towards building more trustworthy AI systems. While promising, challenges remain. Training AI fact-checkers relies heavily on existing datasets, which may not cover all areas of knowledge. Also, focusing solely on factual accuracy doesn't address other issues like bias or the potential for malicious use. Nevertheless, this research highlights the importance of developing robust methods for evaluating and improving the truthfulness of AI-generated content. As LLMs become more integrated into our lives, ensuring they stick to the facts is crucial for building a future where we can trust the information we receive.