During May 2025, Dustin developed a Faithfulness Enrichment Threshold Configuration feature for the fiddler-examples repository, enhancing the LLM Chatbot quickstart by introducing an optional threshold parameter with a default value of 0.5. He focused on improving experiment reproducibility and environment consistency by updating kernelspec and Python version metadata across Jupyter Notebooks. This work leveraged Python and JSON for both feature implementation and notebook metadata management, supporting more reliable and configurable data science workflows. While the scope was focused and no bugs were addressed, Dustin’s contributions enabled better experimentation and parity between development and production environments for LLM operations.