This prompt decomposition technique transformed my LLM outputs

Optimize Your LLM Agent with Divide and Conquer Prompts Learn how to improve the performance and reliability of your LLM agents by using the divide and conquer technique. I demonstrate how to break down a large input prompt into smaller, manageable prompts to reduce variance, simplify debugging, and achieve better results. Using a practical example of grading user-submitted essays, I show how the process can yield more accurate and consistent evaluation by creating JSON metadata, sample solutions, and exercise-wise prompts for the LLM. This strategy can significantly enhance the quality of outputs from your LLM agents. 00:00 Introduction to LLM Agent Issues 00:43 Divide and Conquer Prompts 01:17 Example Task: Grading an Assignment 02:44 Implementing the Solution 05:06 Results and Conclusion