Large language models (LLMs) like ChatGPT are powerful, but they can also make mistakes, sometimes with serious consequences. Researchers are constantly looking for ways to make these models safer and more reliable. A new study explores a clever approach: using LLMs to catch their *own* errors. Imagine an AI "police force" where multiple LLM "checkers" vote on whether a generated output is acceptable. If enough checkers disapprove, the output is rejected and regenerated. This approach leverages the inherent randomness in how LLMs generate text. It's like having several slightly different versions of an LLM double-checking each other's work, reducing the chances of groupthink and catching errors that a single model might miss. The research tested this method using a customer service chatbot scenario where the bot was given a secret password and instructed never to reveal it. Attack prompts designed to trick the bot were used to test its resilience. The results were promising. Using multiple LLM checkers significantly improved the bot's safety and reduced the chances of revealing the password. This "voter-based" approach is especially effective when it's easier to spot bad outputs than it is to create perfect ones, which is often the case in engineering and design. What’s particularly exciting is that this approach might allow simpler, less resource-intensive LLMs to outperform more complex models on specific tasks by effectively policing themselves. This could democratize access to safer, more reliable AI by reducing the need for massive computing power. The researchers are optimistic that with further development, this kind of AI self-regulation could pave the way for safer and more reliable AI systems across various applications.