Large language models (LLMs) are impressive, but they have a vulnerability: jailbreak attacks. These attacks use carefully crafted prompts to trick LLMs into generating harmful or inappropriate content. Think of it like finding a backdoor into a system. Researchers are constantly working on ways to defend against these attacks, and a new method called "Prefix Guidance" offers a promising solution. Instead of complex retraining or input filtering, Prefix Guidance acts like a gentle nudge in the right direction. It works by pre-setting the first few words of the LLM's output, guiding it towards recognizing and refusing harmful requests. For example, starting a response with "I'm sorry, but I cannot…" encourages the model to identify and explain why a request is problematic. This leverages the LLM's existing safety training while using a separate classifier to distinguish between genuine refusals and other types of responses. This approach has been tested across various LLMs and attack methods, showing promising results in blocking malicious prompts. While effective, the method isn't perfect. It can sometimes slightly impact the LLM's overall performance and add a bit of processing time. Future research aims to refine Prefix Guidance, making it even faster and more efficient while preserving the model's helpfulness. This innovative approach is a significant step toward ensuring that LLMs are both powerful and safe.