Worked on stabilizing streaming usage telemetry for the Databricks chat model within the databricks-ai-bridge repository. Addressed a bug that previously caused incomplete or inaccurate token usage data during streaming responses, ensuring that token usage is now reliably captured and emitted as a dedicated end-of-stream chunk across all supported APIs. This backend improvement, implemented in Python, enhances the accuracy of usage analytics and cost tracking by providing consistent telemetry data. Leveraged skills in API integration, backend development, and LLM integration to deliver a clean, low-risk change that improves the reliability of streaming data pipelines and supports better operational visibility.