Radhika worked on the pipecat-ai/pipecat repository, focusing on enhancing the reliability of LLM-driven conversation workflows. She addressed a bug affecting turn progression by ensuring the internal turn completion state was properly reset after each LLM response. This fix involved updating the handling of LLMFullResponseEndFrame, which stabilized the interaction loop and reduced inconsistent behavior during multi-turn exchanges. Using Python and asynchronous programming, Radhika applied careful debugging and unit testing to deliver an end-to-end solution with minimal risk. Her work improved the robustness of LLM workflows, resulting in a smoother user experience and reducing the need for ongoing support intervention.