Sivaprasad worked on enhancing prompt generation capabilities for the mitdbg/palimpzest repository, focusing on robust support for Mixture-of-Agents (MoA) workflows. He refactored the core logic to improve handling of multiple prompt strategies, including critique and refinement, and addressed edge cases in input fields, output schemas, and original outputs. Using Python, he applied principles of API design and software refactoring to increase maintainability and reduce failures in complex prompt orchestration. His work included targeted bug fixes and code cleanup, resulting in more reliable prompt engineering for LLM-based systems and laying a foundation for future feature development and team collaboration.