The importance of logging and tracing in AI development cannot be overstated, as they form the foundation of smooth AI workflows and dependable systems. These practices extend beyond error correction to create a robust framework for continuous evaluation and feedback, enabling teams to understand how AI models make decisions and track their performance over time. Standardized logging formats are crucial for ensuring data remains machine-readable, facilitating cross-team collaboration, and supporting compliance requirements. Effective tracking solutions require both technological infrastructure and organizational commitment, with organizations succeeding in generative AI typically establishing standardized tracking protocols before deployment. As generative systems continue to permeate critical applications, the future of AI development will hinge on robust logging and tracing capabilities that evolve alongside increasingly sophisticated models.