Imagine an AI that could predict legal charges based on a case description. This isn't science fiction anymore. Researchers have developed a groundbreaking new method that injects multi-source heterogeneous knowledge into a prompt learning framework to predict legal charges with unprecedented accuracy. Legal charge prediction is a complex task. Traditional AI models often struggle to grasp the nuances of legal language, the importance of factual elements, and the need to integrate external knowledge. This new research tackles these challenges head-on. The researchers built a system that leverages a legal knowledge base to match specific keywords within a case description. It also taps into the power of a large language model (LLM) and relevant legal articles to extract key factual elements, such as the time, location, individuals involved, and the sequence of events. These factual elements are then encoded into vectors to provide context to the AI model. The system's core innovation lies in how it uses prompt learning. It encapsulates acquired knowledge into prompts, which prime the LLM to reason more effectively about the case. These prompts help the model focus on the most relevant legal information, akin to providing a lawyer with a cheat sheet for the case. Experiments show that this new approach achieves state-of-the-art results on a massive legal dataset, outperforming other baseline methods. Even more impressive, this method demonstrates less reliance on extensive training data, a significant advantage in the legal domain where labeled data can be scarce. While promising, this technology raises important questions about fairness, bias, and transparency. How do we ensure that such systems are used ethically and responsibly? Can we completely eliminate bias from these AI models? Further research is needed to ensure that this technology improves access to justice and strengthens public trust in legal AI tools.