Large language models (LLMs) are impressive, but their safety remains a critical concern. How do we prevent them from generating harmful content? Traditional methods like reinforcement learning from human feedback (RLHF) are effective, but they require extensive datasets and significant computing power. New research explores a more efficient approach called Adversarial Contrastive Decoding (ACD). Imagine training an LLM with two opposing prompts. One encourages safe responses, acting as a "safeguarding prompt." The other pushes the LLM to explore its darker side, an "adversarial prompt." This method helps expose potential harmful outputs without heavy retraining. ACD works by contrasting the responses generated by these opposing prompts during the decoding process. This contrast helps the model learn to filter unsafe content more effectively. Initial tests show ACD can boost safety by over 20% compared to traditional methods, even on models not specifically trained for safety. Moreover, it achieves this without significantly impacting performance on normal tasks. While this research is promising, some challenges remain. ACD requires processing two prompts, which increases computational overhead. Future research aims to improve the efficiency and robustness of ACD, making LLMs safer and more reliable for everyone.