Large Language Models (LLMs) are revolutionizing how we access information, but they're not perfect. One common challenge is ensuring they use the *right* information when answering questions. Think of it like doing research: you wouldn't blindly trust every source you find. You'd prioritize reliable websites over random forum posts. That's where Bayesian inference comes in. It's a statistical method that helps LLMs weigh different pieces of evidence based on their likelihood of being true and their prior credibility. This approach, applied to Retrieval Augmented Generation (RAG), significantly improves the accuracy of LLM-powered systems. Imagine asking an AI about a recent Olympic event. It might find conflicting reports, some accurate and some not. By applying Bayesian principles, the AI can prioritize information from trusted news outlets over less credible sources, leading to more accurate answers. It's like giving the LLM a built-in fact-checker. This method not only improves the quality of responses but also makes the AI's reasoning more transparent. By assigning probabilities to different sources, the AI can explain *why* it chose a particular answer, boosting trust and understanding. This approach is still under development, but it represents a promising step towards more reliable and explainable AI systems.