Large Language Models (LLMs) have shown remarkable abilities, yet they sometimes struggle with complex reasoning tasks, especially smaller, more accessible models. What if there was a way to boost their performance without resorting to massive, resource-intensive models? New research suggests a surprisingly simple yet effective solution: prompt diversity. Instead of asking an LLM the same question repeatedly, researchers are exploring the impact of asking the *same* model slightly *different* questions in parallel. This technique, dubbed "Dipper," creates a virtual ensemble of LLMs, each tackling the problem from a unique perspective. Imagine a team of experts brainstorming; while each individual has their strengths and weaknesses, the combined insights often lead to a more robust and accurate solution. Dipper leverages this principle by feeding diverse prompts to a single LLM, effectively creating a team of virtual experts. The results are impressive. In math reasoning tasks, small models using Dipper outperformed significantly larger models, demonstrating the power of this approach. This breakthrough opens exciting possibilities for improving LLM performance without the need for excessive computational resources, making advanced AI capabilities more accessible to a wider range of users. While challenges remain in optimizing prompt selection and aggregating the diverse responses, this research suggests that the key to unlocking LLM potential may lie not just in bigger models, but in asking smarter questions.