Prompt Tracking, a feature available in Datadog LLM Observability, provides a systematic way to manage and observe changes in prompts used by LLM agents and applications, which are crucial for refining performance and accuracy. This feature allows prompts to be defined, versioned, and monitored as first-class artifacts, enabling teams to apply rigorous management akin to that of application code and models. Prompt Tracking offers the ability to correlate prompt changes directly with performance metrics such as error rate, token usage, and latency, thus facilitating informed rollout decisions based on real data. By integrating with tools like the LangChain framework, it automatically instruments prompt IDs and versions, providing an auditable link between prompt iterations and their performance impacts. The feature also includes a Prompt Playground for validating changes before production, and a consolidated analytics dashboard for tracking prompt versions and trends. This structured approach aligns prompt development with established software practices, enhancing visibility and reducing the risk of regressions in LLM applications.