Google's Gemini models represent a significant leap forward in multimodal AI, particularly in their ability to process and transcribe audio content with remarkable accuracy. However, even advanced models require robust monitoring and evaluation frameworks to ensure consistent quality in production environments. Arize's tracing and evaluation capabilities become invaluable when combined with Gemini's audio transcription prowess. By implementing a complete workflow that generates high-quality transcriptions while tracing each step of the process, developers can gain unprecedented visibility into their audio processing pipelines. This allows teams to identify issues, measure quality, and continuously improve their audio-based AI applications. The tutorial demonstrates how to set up an environment with necessary dependencies and API configurations, configure API credentials, initialize OpenTelemetry tracing infrastructure, prepare an audio sample, implement the core functionality of the application, and evaluate the quality of the transcripts using sentiment analysis. By combining advanced multimodal AI like Gemini with robust observability tools like Arize, developers can build reliable AI systems that deliver high-quality results.