Large language models (LLMs) are impressive, but they don't always get things right. Researchers are constantly looking for ways to improve their accuracy, and a new paper introduces an innovative approach called PEDAL (Prompts based on Exemplar Diversity Aggregated using LLMs). Imagine trying to solve a math problem. Seeing a few examples beforehand can be helpful, but what if those examples aren't quite right or don’t cover all the possibilities? PEDAL tackles this by feeding the LLM a variety of diverse examples, not just one standard set. The LLM then generates multiple potential answers based on these diverse examples, effectively brainstorming different solutions. Finally, the LLM itself acts as a judge, selecting the most consistent and likely answer from its own brainstormed set. This self-evaluation process significantly boosts accuracy on challenging reasoning tasks. The researchers tested PEDAL on two datasets: SVAMP, a set of math word problems, and ARC, a collection of complex science questions. The results are promising, showing that PEDAL outperforms traditional methods in accuracy, while also being more efficient than some more complex techniques. This clever use of diverse prompts and self-aggregation offers a potential pathway to making LLMs even more reliable and efficient problem solvers.