Imagine a world where you could erase your personal information from the internet's memory banks. That future is a step closer with RKLD, a novel method for "unlearning" data from large language models (LLMs). These powerful AI systems, trained on vast amounts of text and code, can sometimes inadvertently memorize sensitive information from their training data. Removing this data while keeping the model useful has been a major hurdle. RKLD tackles this challenge using "knowledge distillation" to selectively unlearn unwanted details. Think of it like a teacher (the ideal, unlearned model) guiding a student (the original LLM) to forget specific information while retaining other important knowledge. This process cleverly leverages "reverse KL divergence," a mathematical tool that helps the AI prioritize forgetting sensitive data over other content. Experiments show RKLD effectively eliminates personal information while maintaining the model's overall capabilities better than existing techniques. While promising, the real-world application of RKLD faces challenges, like adapting to diverse and messy real-world data. Additionally, the output of unlearned models can be unpredictable. Despite these challenges, RKLD presents an exciting advance toward enabling individuals to control their digital footprint in the age of AI.