In the realm of AI development, traditional logging methods fall short in assessing the true effectiveness of systems, leading to what is described as an observability crisis. This issue is addressed through the concept of LLM tracing, which captures a structured, end-to-end record of each significant step in a generative AI workflow, from initial user input to final output. This approach, akin to distributed tracing in microservices, offers a comprehensive view of how different components interact and produce results, highlighting not only the sequence and duration of operations but also their interdependencies and potential failures. Challenges specific to large language models (LLMs), such as non-deterministic behaviors, semantic failures, hallucinations, and biases, are better managed through this method. LLM traces enhance understanding by connecting all related events into a coherent narrative, capturing core span data, performance metrics, model configurations, and user feedback. This comprehensive tracing allows for more precise debugging, optimization, and cost tracking, and aids in compliance by providing an immutable audit trail. Human-in-the-loop evaluation complements this by ensuring nuanced judgment and creating strategic datasets that enhance model performance. By embracing these practices, AI development shifts from reactive problem-solving to proactive quality assurance, with tools like Opik facilitating detailed tracing and evaluation across application stacks.