Markov Chains are a mathematical system that undergo transitions from one state to another on a state space. They are 'memoryless' processes, meaning the next state depends only on the current state and not on the sequence of events that preceded it. The visual explanation makes it easier for readers to grasp how these chains work, demonstrating concepts like state transitions, probabilities, and stationary distributions. The comments indicate an interest in comparing traditional Markov chains with more advanced models like Large Language Models (LLMs), hinting at LLMs' capacity to handle context and memory more effectively than standard Markov processes due to their architecture and training on vast datasets.